Custom femoral stem design plays a critical role in total hip arthroplasty (THA) to create an implant that is well positioned and well fitting for long-term function. However, routine practice employs generic size and minimum load testing. This leads to complications of stress shielding, poor fit, or early implant failure. This project created an online system that designs and simulates femoral stem designs from patient-specific information. Clinicians and engineers are able to enter parameters such as femur length, bone density, and body weight through an interactive user interface. A Flask backend API processes the input, selects the most suser interfacetable design among seven validated stem designs, and initiates automated simulations within ANSYS Workbench. These simulations calculate stress, displacement, and safety factors. For instance, a patient weighing 70 kg produced a head offset of 28.7 mm, taper ratio of 1.47, and stem length ratio of 37.5%. The simulated gait induced a stress of 185.3 MPa, displacement of 0.156 mm, and a safety factor of 2.8—all within clinical tolerance. Compared with traditional methods, this system improves predictive accuracy by 43% for heavier patients through adjusting loads based on real body weight. Five activities of daily living are also analysed, instead of one, and they discover that climbing stairs causes more than three times the stress caused by walking. The design process is also sped up, with 95% less time for iteration and no need for costly physical models. Real-time feedback and AI-optimized design matching also improve implant safety and fit.