Homology modeling using MODELLER has been a cornerstone of computational structural biology for over three decades, yet its protocols, advances, and computational challenges have not been fully reviewed in the context of modern structural prediction methods. Despite breakthroughs from deep learning approaches like AlphaFold, MODELLER’s satisfaction of spatial restraints methodology remains vital in template-based modeling, especially in low sequence identity cases, multi-template strategies, and specialized applications. This review critically examines current MODELLER protocols, recent methodological improvements, and persistent computational challenges. We analyzed literature on MODELLER applications, benchmarking, and algorithmic refinements, focusing on template selection, alignment optimization, model validation, and integration with modern computational methods. Our analysis shows that although MODELLER’s core algorithm has remained largely unchanged since the 1990s, advances in template selection, multi-template modeling, and statistical potentials such as DOPE scoring have improved model accuracy. Recent work highlights the impact of optimized σ value estimation and incorporation of statistical potential terms, particularly in multi-template modeling. Nonetheless, challenges persist, including reduced accuracy in low sequence identity modeling, scalability issues, and poor handling of conformational flexibility. MODELLER retains unique strengths in template-based modeling, particularly when combined with modern validation strategies and machine learning–assisted template selection. Looking ahead, progress in hybrid modeling approaches, more accurate scoring functions, and greater computational efficiency will be essential for MODELLER to sustain its relevance within the rapidly evolving field of protein structure prediction.