This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in driving the dairy industry towards net-zero emissions, a critical objective in the global fight against climate change. Using the Canadian dairy sector as a case study, the study demonstrates the global applicability of these technologies in enhancing environmental sustainability across agriculture. The paper begins by outlining the environmental challenges faced by the dairy industry, particularly greenhouse gas (GHG) emissions from enteric fermentation and manure management. It emphasizes the need for innovative approaches to address these challenges, given the accelerating climate crisis. The analysis delves into how Big Data and AI can revolutionize emission management in dairy farming. These technologies are applied in optimizing feed efficiency, refining manure management, and improving energy utilization. Specific applications include predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring. The paper also addresses the broader implications of integrating these technologies, such as the development of benchmarking standards for emissions, the importance of data privacy, and the role of policy in promoting sustainable practices. The Canadian dairy industry's environmental footprint is characterized by emissions from enteric fermentation and manure management, which are critical factors in the broader context of climate change. The industry faces challenges in reducing these emissions through technological, economic, and policy barriers. To address these challenges, the paper suggests a multifaceted strategy that includes technological innovations, effective farm management practices, and supportive policy frameworks. The integration of Big Data and AI in dairy farming is highlighted as a transformative leap towards sustainability and efficiency. These technologies enable data-driven decision-making, optimize resource usage, and enhance herd health. The paper also discusses the economic and structural landscape of the Canadian dairy industry, including its size, global market presence, farm structures, and regulatory framework. Strategies for reducing GHG emissions in dairy farming are outlined, including optimized feed efficiency, advanced manure management, pasture-based farming, precision agriculture, biogas systems, and carbon capture and storage (CCS) technologies. The role of policy initiatives and incentives, such as federal and provincial incentives, carbon pricing, research and development support, and extension services, is emphasized. The future of dairy farming with Big Data is explored, emphasizing the integration of advanced sensors, data analytics, and predictive modeling. The paper also discusses the integration of IoT and robotics, the policy and industry implications of technological advancements, and the role of digital technology in enhancing emission efficiency. Artificial Intelligence (AI) is identified as a pivotal tool in improving emission management and energy efficiency in dairy farming. AI applications include feed optimization, manure management, and energy use reduction. The paper outlines immediate and long-term strategies for implementing AI and Big Data in dairy farming, emphasizing the need for cross-disciplinary collaboration, policy support, and continuous improvement. Finally, the paper addresses the challenges and future directions for the integration of AI in dairy farming, highlighting the importance of overcoming current challenges and continuing innovation to achieve sustainable and environmentally responsible practices.This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in driving the dairy industry towards net-zero emissions, a critical objective in the global fight against climate change. Using the Canadian dairy sector as a case study, the study demonstrates the global applicability of these technologies in enhancing environmental sustainability across agriculture. The paper begins by outlining the environmental challenges faced by the dairy industry, particularly greenhouse gas (GHG) emissions from enteric fermentation and manure management. It emphasizes the need for innovative approaches to address these challenges, given the accelerating climate crisis. The analysis delves into how Big Data and AI can revolutionize emission management in dairy farming. These technologies are applied in optimizing feed efficiency, refining manure management, and improving energy utilization. Specific applications include predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring. The paper also addresses the broader implications of integrating these technologies, such as the development of benchmarking standards for emissions, the importance of data privacy, and the role of policy in promoting sustainable practices. The Canadian dairy industry's environmental footprint is characterized by emissions from enteric fermentation and manure management, which are critical factors in the broader context of climate change. The industry faces challenges in reducing these emissions through technological, economic, and policy barriers. To address these challenges, the paper suggests a multifaceted strategy that includes technological innovations, effective farm management practices, and supportive policy frameworks. The integration of Big Data and AI in dairy farming is highlighted as a transformative leap towards sustainability and efficiency. These technologies enable data-driven decision-making, optimize resource usage, and enhance herd health. The paper also discusses the economic and structural landscape of the Canadian dairy industry, including its size, global market presence, farm structures, and regulatory framework. Strategies for reducing GHG emissions in dairy farming are outlined, including optimized feed efficiency, advanced manure management, pasture-based farming, precision agriculture, biogas systems, and carbon capture and storage (CCS) technologies. The role of policy initiatives and incentives, such as federal and provincial incentives, carbon pricing, research and development support, and extension services, is emphasized. The future of dairy farming with Big Data is explored, emphasizing the integration of advanced sensors, data analytics, and predictive modeling. The paper also discusses the integration of IoT and robotics, the policy and industry implications of technological advancements, and the role of digital technology in enhancing emission efficiency. Artificial Intelligence (AI) is identified as a pivotal tool in improving emission management and energy efficiency in dairy farming. AI applications include feed optimization, manure management, and energy use reduction. The paper outlines immediate and long-term strategies for implementing AI and Big Data in dairy farming, emphasizing the need for cross-disciplinary collaboration, policy support, and continuous improvement. Finally, the paper addresses the challenges and future directions for the integration of AI in dairy farming, highlighting the importance of overcoming current challenges and continuing innovation to achieve sustainable and environmentally responsible practices.