<![CDATA[This study presents a Systematic Literature Review (SLR) of data-driven predictive models for delivery delay prediction in logistics and transportation systems. A total of 508 articles were initially retrieved from the Scopus database (IEEE, Elsevier, MDPI, Springer), which served as the primary source of literature, and were systematically evaluated using the PRISMA framework through the identification, screening, eligibility, and inclusion stages, resulting in 55 selected studies published between 2016 and 2026. This review addresses two research questions: (RQ1) how data-driven predictive models, including machine learning and modern statistical approaches, are developed and applied to predict delivery delays; and (RQ2) how these models perform under varying operational conditions. The findings reveal the dominance of machine learning, deep learning, and hybrid models, which leverage heterogeneous data sources such as GPS, AIS, traffic, weather, and IoT data to capture complex spatio-temporal dependencies. Hybrid and deep learning approaches generally demonstrate superior predictive performance in dynamic and nonlinear environments, whereas conventional methods offer advantages in interpretability and computational efficiency. Model performance is strongly influenced by operational conditions, including congestion, weather variability, prediction horizon, and data quality. Inconsistent evaluation metrics limit cross-study comparability, while context-specific datasets reduce model generalizability. Dependence on historical data further constrains adaptability in real-time and disruption-prone environments. This study provides a structured synthesis of predictive modeling approaches, performance trends, and research gaps, offering guidance for researchers and practitioners in selecting and developing delivery delay prediction models. Future research should focus on integrating underutilized contextual and human-related variables, real-time multi-source data, and multi-objective optimization techniques to improve model robustness, scalability, and real-world applicability in intelligent logistics systems.]]>
