Autologous fat grafting (AFT) has become a widely used technique in breast reconstruction, offering natural aesthetics, tissue integration, and patient satisfaction. However, its clinical outcomes require comparison with implant-based reconstruction (IBR), the most common method in clinical practice. While AFT provides a more natural appearance and avoids foreign body-related complications, issues such as fat resorption, procedural variability, and oncological concerns necessitate further investigation. Additionally, artificial intelligence (AI) has been increasingly integrated into breast imaging and reconstructive planning, improving diagnostic accuracy, procedural optimization, and complication prevention. This study aims to compare AFT and IBR while exploring AI's role in enhancing breast reconstruction outcomes. A comprehensive review of clinical studies was conducted to evaluate the advantages, limitations, and oncological implications of AFT versus IBR. AI-driven applications in breast imaging and reconstructive planning were examined for their potential in predicting fat graft retention and optimizing implant selection. Data from systematic reviews and meta-analyses were incorporated to refine reconstruction strategies. AFT offers superior aesthetic outcomes with better tissue integration but presents variability in fat resorption. IBR remains the preferred approach due to its predictability but carries risks of implant-related complications. AI technologies contribute to improved reconstruction planning, enhancing surgical precision and long-term patient outcomes. Optimized patient selection and long-term follow-up are essential for improving breast reconstruction techniques. AI-driven approaches provide valuable tools for enhancing procedural predictability and personalized treatment strategies. Future research should focus on refining AI algorithms and establishing standardized protocols for reconstructive decision-making.