Artificial Intelligence (AI) In Animal Health Market Size is valued at 1351.10 Mn in 2024 and is predicted to reach 7758.09 Mn by the year 2034 at a 19.2% CAGR during the forecast period for 2025-2034.
Artificial intelligence (AI) has made substantial advances in various disciplines, including healthcare, and has just begun to impact animal health. AI in animal health entails the application of machine learning algorithms and other AI approaches to improve veterinarian treatment, animal welfare, and research. Artificial intelligence is used to track and monitor wildlife populations, assisting conservation efforts and management plans.
Researchers collected data on endangered animals and their habitats using AI-enabled drones, camera traps, and other technologies. Rapid advances in machine learning techniques, particularly deep learning, enabled better animal health pattern detection and predictive modelling. AI algorithms were growing more sophisticated and capable of digesting vast animal health data.
However, The supply of drugs, vaccines, and testing equipment is critical to the animal health sector. Travel limitations and manufacturing issues may have disrupted the global supply chain, affecting the availability of certain AI-powered diagnostic equipment and technologies.
Artificial Intelligence (AI) In Animal Health Market is segmented as solution, application, and animal type. According to the solution segment, the market is segmented into Hardware, Software & Services. The application segment includes Diagnostics, Identification, Tracking & Monitoring, and Others. By animal type, the market is segmented into Diagnostics, Identification, Tracking, Monitoring, and Others.
The Software & Services division is expected to hold a major share in the global Artificial Intelligence (AI) In Animal Health Market in 2022. Continuous advancements in software technologies, including sensors, imaging devices, computer vision, deep learning, and wearables are fueling the rise of the categories. These solutions are becoming more accessible to animal owners because of improved performance, miniaturization, higher computing power, and cost savings. Increased cooperation between software developers, technology businesses, veterinary clinics, research institutions, and industry groups is also contributing to market growth.
The Diagnostics segment is likely to grow at a rapid rate in the global Artificial Intelligence (AI) In Animal Health Market. The increasing integration of AI in veterinary diagnostics, the availability of AI-powered diagnostic solutions, and the need to increase diagnostic capabilities in animal health all contribute to this expansion. Some of the leading market companies in AI in the veterinary diagnostics sector include IDEXX, Zoetis, SignalPET, and Vetology LLC.
The North America Artificial Intelligence (AI) In Animal Health Market is estimated to witness the highest market revenue in the near future. To assist veterinarians in diagnosing diseases and abnormalities, AI systems can analyze radiographs, CT scans, and MRI pictures. These algorithms can discover patterns and signals humans may miss, resulting in greater accuracy and faster diagnosis.
The expanding integration of AI in veterinary diagnostics and modern veterinary healthcare infrastructure in the U.S. and Canada are important factors driving the regional share. Due to the expanding animal population and activities by local market players, Asia Pacific is likely to increase rapidly. For example, Alibaba Cloud's ET Agricultural Brain applies the company's in-house AI technology to agriculture.
| Report Attribute | Specifications |
| The Market Size Value In 2024 | USD 1351.10 Mn |
| Revenue Forecast In 2034 | USD 7758.09 Mn |
| Growth Rate CAGR | CAGR of 19.2% from 2025 to 2034 |
| Quantitative Units | Representation of revenue in US$ Million and CAGR from 2025 to 2034 |
| Historic Year | 2021 to 2024 |
| Forecast Year | 2025-2034 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Components, By Verticals, By Application |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany;India; Japan; Brazil; Mexico ;The UK; France; Italy; Spain; China; South Korea; South East Asia |
| Competitive Landscape | Alpha Phenomics Inc., Animals.ai, EIO Diagnostics Inc, Farm4Trade, FarmSee, Halter USA Inc., Heska Corporation, IDEXX Laboratories, Inc., ImpriMed, Inc., IMV Technologies, Kraal (UK), Merck & Co., Inc., Moichor Inc., OneCup AI, OPTIFARM, Petriage, Pondus Limited, porklogic.ai, Serket SignalPET, TARGAN Inc., Vet-AI, VetCT, Vetology LLC, Zoetis Services LLC, Other Prominent Players. |
| Customization Scope | Free customization report with the procurement of the report, Modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
Artificial Intelligence (AI) In Animal Health Market By Solution-
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Artificial Intelligence (AI) In Animal Health Market By Animal Type-
By Region-
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This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.