Obesity is a global health crisis, with bariatric surgery considered a highly effective intervention for sustained weight loss and resolution of associated health conditions. Despite its benefits, some patients experience postoperative complications, emphasizing the importance of accurate risk prediction. Traditional models often lack the capacity to manage complex clinical data. Artificial intelligence (AI) offers transformative potential for improving the prediction of surgical complications. This systematic review synthesizes existing research on AI's role in forecasting complications following bariatric surgery. The review followed PRISMA 2020 guidelines, with searches conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for studies examining AI applications in this context. Seven retrospective cohort studies were included, and data were extracted on study design, AI algorithms, and outcomes. Risk of bias was assessed using PROBAST, and a narrative synthesis was conducted due to study heterogeneity. The included studies showed variability in AI model performance, with ensemble methods and neural networks generally performing better than traditional logistic regression. Reported area under the curve (AUC) values varied widely, with higher accuracy noted for predicting specific complications such as diabetes and leaks. Key challenges included overfitting, data imbalance, and limited generalizability, especially in deep learning models. Most studies were conducted in Sweden and the United States, utilizing large datasets that may introduce regional biases. Overall, AI shows promise in enhancing complication prediction in bariatric surgery, though methodological limitations highlight the need for prospective, multicenter validation. Future research should focus on addressing data imbalance, refining feature selection, and facilitating the clinical integration of AI through decision-support systems to improve patient care.