This chapter delves into the innovative integration of process-based models, machine learning techniques, and remote sensing to significantly improve the precision and reliability of yield gap assessments. Yield gaps—the disparities between potential and actual crop yields—serve as critical indicators for understanding the underlying limitations in agricultural productivity and for formulating strategies to close these gaps. Dynamic models, such as APSIM and DSSAT, ensure a comprehensive mechanistic framework that simulates crop growth, development, and environmental interactions under varying management practices and climatic conditions. These models are invaluable for estimating potential yields across diverse agroecosystems. On the other hand, machine learning excels in processing large datasets, uncovering complex patterns, and offering predictive insights that traditional methods may overlook. When paired with remote sensing, which provides high-resolution and robust data on crop health, growth stages, and environmental conditions, this integrated approach addresses the spatial and temporal variability inherent in agricultural systems. By combining the strengths of these technologies, this chapter proposes a robust methodology for yield gap assessments, offering improved predictive accuracy and scalability. This fusion of advanced techniques not only enhances the precision of yield forecasts but also provides actionable insights for policymakers and agricultural stakeholders, enabling the development of targeted, sustainable interventions that are essential for future food security in the face of resource constraints and climate change.