This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in advancing the dairy industry toward net zero emissions, a critical goal in the global fight against climate change. Using the Canadian dairy sector as a case study, the research demonstrates the global applicability of these technologies in enhancing environmental sustainability. The paper addresses the environmental challenges facing the dairy industry, particularly greenhouse gas (GHG) emissions from enteric fermentation and manure management. It examines the role of Big Data and AI in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are analyzed. The paper also discusses the broader implications of integrating these technologies, including the development of benchmarking standards for emissions, data privacy, and the importance of policy in promoting sustainable practices. It concludes that the dairy industry can achieve environmental sustainability through a combination of technological innovation, farm management practices, and supportive policy frameworks. The integration of Big Data and AI in dairy farming offers a pathway to reduce emissions, enhance efficiency, and align with global environmental sustainability efforts.This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in advancing the dairy industry toward net zero emissions, a critical goal in the global fight against climate change. Using the Canadian dairy sector as a case study, the research demonstrates the global applicability of these technologies in enhancing environmental sustainability. The paper addresses the environmental challenges facing the dairy industry, particularly greenhouse gas (GHG) emissions from enteric fermentation and manure management. It examines the role of Big Data and AI in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are analyzed. The paper also discusses the broader implications of integrating these technologies, including the development of benchmarking standards for emissions, data privacy, and the importance of policy in promoting sustainable practices. It concludes that the dairy industry can achieve environmental sustainability through a combination of technological innovation, farm management practices, and supportive policy frameworks. The integration of Big Data and AI in dairy farming offers a pathway to reduce emissions, enhance efficiency, and align with global environmental sustainability efforts.