Artificial intelligence (AI) in crop science is redefining the agriculture issue by being accurate, scalable, and predictive. It is an overview of the recent developments in AI-based crop modeling in the context of its advancement, management, and sustainability. We criticize the application of machine learning (ML), deep learning (DL), reinforcement learning (RL) and computer vision to fields of high-throughput phenotyping, genomic prediction, yield forecasting and stress detection. Convolutional neural networks and vision transformers have assisted in new developments in image-based prediction of characteristics of UAVs, satellites, and ground sensors, and recurrent and graph neural networks to new developments in spatiotemporal modeling of crop-environment interactions. This is possible by combination of predictive modeling and crop simulation systems and enables dynamic decision support of the changing climatic conditions. Moreover, explainable AI (XAI) technique is also in progressive use to increase transparency of models and make them acceptable to breeders and farmers. However, there are still serious obstacles like the heterogeneity of the data, models transferability is not applicable across the regions, annotation bottlenecks, and the failure to incorporate the biological knowledge into the AI structures. The other fact, which we highlight, is the unavailability of AI to smallholder systems and the uniformity of standard and open-source datasets. Future directions It concentrates on the use of multi-omics, remote sensing, and on-farm data in individual AI systems, and physics-informed and hybrid modelling. Such integrative practices are necessary to make AI tools more powerful, decipherable and scalable. Ultimately, the strategic application of next generation AI models will be in sustainable increment, resultant reduction in environmental footprints, and crop production systems in a manner that will be resilient to the changing climatic conditions in order to feed the ever-growing world population which is increasing at an accelerated rate.