The development and optimization of molecular force fields are central to computational chemistry, providing essential tools for simulating atomic interactions. Classical molecular force fields, such as Assisted Model Building and Energy Refinement and Chemistry at HARvard Macromolecular Mechanics, rely on simplified mathematical models to describe phenomena like bond stretching, angle bending, torsional rotations, and non-bonded interactions. While effective for many applications, these models face limitations in accounting for polarization effects, multi-body interactions, and chemical reactions. Machine learning (ML)-based force fields have emerged as a solution, combining the accuracy of quantum mechanical methods with the efficiency of classical approaches. Models such as symmetric gradient domain machine learning improve potential energy surface predictions and enable the detailed study of complex systems, including chemical reactions and thermodynamic properties. However, challenges remain in parameterization, data quality, and embedding physical principles into these models. Hybrid approaches that integrate classical and ML-based methods hold a significant potential for enhancing predictive accuracy and expanding the applicability of molecular force fields. These advancements promise to improve dynamic simulations, support drug discovery efforts, and address broader challenges in molecular modeling.