Sugarcane production is strongly influenced by the interaction among climate variability, soil properties, and management practices. This chapter synthesizes current advances in agrometeorology and process-based crop modeling to assess climate change impacts and support adaptive management in sugarcane systems. Process-based models (PBMs), such as DSSAT/CANEGRO, APSIM-Sugar, and DSSAT/SAMUCA, are highlighted for their ability to simulate soil–plant–atmosphere interactions and represent genotype × environment × management dynamics under both current and future conditions. Applications include the evaluation of climate change scenarios, optimization of management practices, and yield forecasting using data assimilation and remote sensing. Results across diverse environments indicate that climate variability remains the dominant driver of yield fluctuations, while management practices offer significant opportunities to improve productivity and resource-use efficiency. Despite advances, limitations remain in representing stress factors and physiological responses. Future developments should focus on improving model processes, integrating large-scale datasets, and enhancing decision-support capabilities.