This paper proposes the use of AI assistance in the development of medical implants with distinct bone-like structure using the magnesium alloy, Ti-6Al-4 V (Ti64). This alloy is organized into a variety of bone-like structures through the use of a manufacturing technique called selective laser melting (SLM). SLM is able to produce a precise lattice structure similar to bone porosity compared to other additive manufacturing techniques, however predicting healing outcomes based on factors involved in bone regeneration has proven to be computationally demanding and requires expert knowledge. AI however, is able to use existing finite element analysis (FEA) data as well as demographic data to predict personalized bone healing patterns for individuals. Multiple Ti64 samples were first generated, with varying volumetric energy density (VED) levels, then they were analyzed to determine optimal levels for reducing defects while maintaining mechanical performance. Tensile tests were then performed on Ti64 samples using process parameters like laser power, hatch spacing, etc. to get data on elastic modulus, yield strength, and ultimate tensile strength, which were then incorporated into the simulation. A finite element (FE) model was generated using Abaqus CAE to simulate a tibial fracture with an intramedullary nail fixation (IMN), with multiple factors integrated into the model to accurately portray bone healing as well as the modulus of implants. These bone healing factors included gradual load bearing as a necessary stimulus for callus formation, a biological tissue expansion surrogate called a coefficient of thermal expansion (CTE), an equation to describe the developing shape of the bone using biomechanical stimulus regulated growth, and mesenchymal stem cell (MSC) current concentration, max concentration, and rate of proliferation. All of these factors were included in the simulation which ran for a total of 112 days. There was a trend between lower modulus implants and greater callus formation, with a correlation for higher callus volume between greater healing time and lower implant modulus. Finally machine learning (ML) was done using the finite element simulation data, with XG Boost Regression, Random Forest Regression, and Gradient Boosting Regression being the most accurate ML algorithms. Each of the algorithms had respective R squared values above 97% and have the potential to facilitate not only predictive analysis of outcomes, but also to reverse engineer mechanical specifications based on current biological factors like MSC concentration. Artificial neural networks (ANN) were also tested, with TensorFlow reaching almost a R squared value of 98% when set to correct parameters. Overall this paper is technically rigorous and approaches the optimization of orthopedic implants using SLM from various angles, as well as assesses the characteristics and capabilities of AI, ML, and ANN with similarly rigorous testing.