Amid climate change, global agriculture faces one of the toughest challenges in human history and serious threat to food security. Rapidly changing environmental conditions are exerting unprecedented stress on natural ecosystems and food production systems. Temperature extremes in the form of high temperature stress or very low temperature stress, unexpected and continuous precipitation leading to sever floods, prolonged drought, salinization of the soil, soil nutrient deficiency, degradation of soil health, ever increasing population load and war or war like global situations, pose serious threat to the global agriculture sector. Furthermore, the intensification of insect infestations, coupled with the frequent occurrence of bacterial and fungal diseases, adds new layers of complexity to crop protection and food security. The above said environmental cues highlight the urgent need for transformative solutions that go beyond traditional farming method. Therefore, it becomes imperative to employ the advancements in Artificial Intelligence (AI) including deep learning (DL) and machine learning (ML) approaches in modern agricultural practices to reshape conventional agriculture for sustainable development. AI aided agriculture tools are now well established for real time monitoring of environmental fluctuations, early and accurate detection of diseases and nutrient deficiency, prediction of plant stress responses, optimization of resource utilization, and development of adaptive strategies to ensure sustainable crop production. Using precision farming practices, data-driven insights, and predictive analytics, AI strengthens the global food security and also enhances crop resilience to climate change.