Free energy calculations play a vital role in understanding protein behaviour at the molecular level. From protein folding and stability to ligand binding and enzyme catalysis, these calculations provide quantitative insights that are indispensable for both basic research and practical applications in fields such as drug discovery, protein engineering, and biotechnology. Due to the complex energy landscapes of proteins, traditional molecular dynamics simulations often fail to explore rare events or overcome high-energy barriers effectively. Enhanced sampling techniques, such as metadynamics, umbrella sampling, or replica exchange molecular dynamics, were developed to address these challenges, allowing for more efficient exploration of conformational space and improved accuracy in free energy predictions. These methods accelerate the sampling of relevant states and transitions, making it feasible to capture rare but biologically significant events. Recently, machine learning has also begun playing a crucial role in enhancing sampling efficiency, reducing the need for extensive computational resources. As computational power continues to increase and machine learning techniques are integrated with enhanced sampling algorithms, the scope and accuracy of free energy calculations will significantly improve, opening new avenues for more precise understanding and prediction of molecular interactions and biological processes.