In the rapidly evolving landscape of e-commerce, online shopping has gradually gained widespread acceptance. Yet, physical retail channels continue to exert a significant influence on product sales. Within this framework, sales forecasting occupies a pivotal position in traditional commerce and is indispensable for guiding corporate strategy formulation. However, most existing forecasting models fail to fully harness the potential impact of multi-source external information on consumer purchasing behavior. To address these challenges, this study proposes a novel sales forecasting model that synergizes the Extreme Gradient Boosting (XGBoost) algorithm with multi-source data integration techniques. Not only have we comprehensively collected an extensive range of external data, but we have also integrated the embedding and aggregation techniques of Point of Interest (POI) data to enrich the set of external information features available to the model. Leveraging feature engineering techniques, these heterogeneous data are transformed into formats amenable to model training. Relying on the nonlinear modeling capabilities of XGBoost and its efficacy in handling large-scale datasets, we trained and optimized the model. Empirical results indicate that our proposed method, predicated on multi-source data integration, significantly outperforms traditional models based on a single data source, thereby enhancing prediction accuracy and providing more precise inventory management strategy support for businesses.