In prosthetic development, particularly for individuals with transtibial amputations, the accurate simulation and replication of natural human gait mechanisms pose significant challenges. This paper addresses these challenges by validating a novel musculoskeletal model designed for transtibial prosthetic users using advanced simulation environments such as MyoSuite, OpenSim, and Scone. The study aims to identify the strengths and limitations of each platform in representing the biomechanical properties of prosthetic gait, compare their kinematic accuracy, and analyze the effectiveness of machine learning algorithms in generating stable and adaptive gait patterns. Initial static motion tests demonstrated that the models could maintain balance for 10 seconds, with effective tracking of the Center of Mass (CoM). Disturbance tests further highlighted the models' ability to respond adaptively to external forces. The results indicate that while OpenSim excels in detailed musculoskeletal modeling, MyoSuite and Scone provide superior adaptability and real-time control capabilities, especially when enhanced with reinforcement learning algorithms. These findings contribute valuable insights for the design and development of more effective prosthetic devices, ultimately improving the quality of life for individuals with transtibial amputations.