Machine learning in pet food: a comprehensive review of applications, challenges, and future directions

This is the authors' abstract. We don't add key points for this paper.

The global pet food industry is rapidly evolving with the integration of machine learning (ML) technologies. ML plays a crucial role in optimizing ingredient formulation, enhancing quality control, personalizing nutrition, and predicting consumer preferences. The use of deep learning, reinforcement learning, and natural language processing (NLP) is transforming pet food manufacturing by improving efficiency and ensuring better health outcomes for pets. This review explores the key applications of ML in pet food science, discusses current challenges, and highlights future directions. The paper also presents a comparative analysis of different ML techniques used in the pet food sector. Machine learning is transforming the pet food industry by optimizing ingredient formulation, improving quality control, and predicting consumer preferences. However, widespread AI adoption faces challenges, including data limitations, regulatory requirements, computational expenses, and consumer trust concerns. The future of AI-driven pet food innovation lies in explainable AI, blockchain-integrated supply chains, IoT-enabled pet health monitoring, and synthetic data-powered machine learning models. As technology advances, AI will play a key role in providing safer, healthier, and more personalized nutrition for pets, shaping the industry's future.