The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into the agri-food industry has undoubtedly changed the way food is produced, processed, and distributed. These technologies have led to unprecedented improvements in productivity, efficiency, and sustainability that promise to address pressing issues such as food security, resource management, and supply chain optimization. However, the quick adoption of AI and ML in the agri-food industry raises several ethical issues, which must be recognized and addressed despite impressive advancements. Questions about accountability, transparency, and justice in these systems surface when AI algorithms make judgments on their own using enormous datasets and intricate calculations. Biases within algorithms that may stem from skewed datasets lead to unequal treatment or opportunities for certain farmers, producers, or consumers. Additionally, these technologies in agri-food sector introduce complex socio-economic dynamics, particularly affecting rural communities, smallholder farmers, and labourers. Crafting ethical frameworks is essential for safeguarding consumer safety and well-being, setting standards for food quality, and safety. These frameworks also address concerns surrounding data privacy, algorithmic bias, and ethical treatment of animals thus establishing guidelines for responsible practices in these areas. Designing regulatory policies with clear guidelines can stimulate innovation and build trust among stakeholders, facilitating the acceptance and adoption of AI and ML technologies in the agri-food industry. Evaluating the potential risks and benefits of AI and ML in the agri-food sector, this book chapter provide insights into the need to develop ethical frameworks and regulatory policies to ensure responsible and equitable deployment of these technologies.