Artificial intelligence (AI) may be a game-changer for farmers coping with the escalating problems of climate change. AI models can predict and mitigate the wide-ranging impacts of climate change on agriculture, providing farmers with state-of-the-art tools to aid in their decision-making. As environmental issues intensify, AI integration is starting to shift the game for climate-resilient agriculture. In order to manage the complexities of climate unpredictability, the chapter discussed how AI may assist farmers in making adaptive decisions in this scenario. The advantages of AI and climate research working together in concert to identify climate-related risks, such as extreme weather, changed precipitation patterns, and new pest concerns It also highlights the impact of AI on smallholder and rural farmers in an effort to increase overall resilience. Someone has to take initiative, manage irrigation, distribute resources, and pick crops carefully. A thorough analysis is conducted of the possible advantages and challenges of widespread AI application in various agricultural situations. For scholars, policymakers, and business leaders who want to address resilient and sustainable farming practices for the benefit of future generations while simultaneously advancing AI in agriculture Crop yields, soil health, and water availability will all be impacted by climate change, which presents significant difficulties for global agriculture. Artificial intelligence (AI) and machine learning (ML) are emerging as game-changing technologies for developing climate-resilient agriculture in response to these problems. AI-powered technologies that combine computer vision, deep learning, reinforcement learning, and predictive analytics provide precise climate forecasting, early disease detection, and economical resource utilization. Machine learning techniques such as support vector machines (SVMs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs) enhance crop monitoring, yield prediction, and soil quality assessment. Additionally, reinforcement learning and Internet of Things (IoT) integration enable smart irrigation systems and adaptive decision-making in unexpected climate conditions. This research provides a comprehensive analysis of the use of AI and ML in precision agriculture, climate-smart farming, and sustainable land management. We discuss the most recent advancements in self-sufficient farming, remote sensing, and geospatial analysis that contribute to increased climate change resilience. The study also looks at ethical concerns with the use of AI, interpretability of models, and data scarcity. Edge AI, blockchain-based agricultural intelligence, and federated learning are some of the developing concepts that the report highlights as potentially useful for future climate-resilient farming systems.