Integrating machine learning (ML) algorithms into crop modeling offers a transformative approach to enhancing agricultural systems' predictive accuracy and adaptability amid climate change pressures. This article examines recent advancements in ML applications within crop modeling, emphasizing improvements in real-time monitoring, scenario analysis and climate-resilient strategy development. Key ML methods-including supervised, unsupervised, and deep learning-are explored for analyzing extensive datasets such as remote sensing imagery, meteorological data, and soil properties. These methods enable precise predictions of crop growth and yield, support early detection of climate-induced stressors and allow for simulation of future climate impacts. Additionally, ML-driven optimization of crop management, including planting schedules and resource allocation, fosters effective adaptation strategies, enhancing crop resilience and mitigating greenhouse gas emissions. Despite these advancements, challenges persist related to data quality, model interpretability and the scalability of ML applications across diverse agricultural settings.