Background <p>Advancements in artificial intelligence (AI) are transforming surgical practices by enhancing precision in preoperative planning, decision-making, and patient outcomes. Despite these advancements, its application in breast reconstruction—a complex and growing field—remains underexplored. This study aims to explore the implementation of AI in breast reconstruction by analyzing the characteristics of AI research, the reported types and applications of AI systems, and their clinical implications and outcomes.</p> Methods <p>A scoping review was conducted of MEDLINE (PubMed) and Scopus databases to identify original studies of AI implementation in breast reconstruction since 2004. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized.</p> Results <p>This review analyzed 344 articles, selecting 34 after screening. The majority (65%) were published recently, with the U.S. leading in contributions. Retrospective cohorts dominated study designs. AI applications included predictive modeling, diagnostic support, and surgical planning, primarily using supervised machine learning, natural language processing, and neural networks. Outcomes focused on complication prediction and educational tools, highlighting AI’s emerging role in breast reconstruction for personalized, data-driven approaches in clinical practice.</p> Conclusions <p>Artificial intelligence is advancing the field of breast reconstruction by enhancing preoperative planning, decision support, and postoperative complication prediction. While promising, further validation across diverse populations is necessary to confirm these models’ effectiveness. Continued research and technological evolution are essential for integrating AI-driven precision and efficiency into reconstructive surgery, ultimately improving patient outcomes and clinical decision-making.</p> Level of evidence <p>Not gradable.</p>