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International Journal of Advanced Innovative Technology in Engineering (IJAITE)Machine Learning and Deep Learning Approaches for Breast Cancer Recurrence Prediction: A Systematic Literature Review Prachi Damodhar Shahare, Abha Mahalwar Abstract : Breast cancer recurrence remains a major clinical challenge despite advances in diagnosis and treatment, significantly affecting long-term survival and patient management. Accurate recurrence prediction is essential for personalized therapy planning and optimized follow-up strategies. In recent years, artificial intelligence–driven approaches, including machine learning, deep learning, hybrid, and ensemble models, have been widely explored to address limitations of traditional prognostic methods. This study presents a systematic literature review of computational models developed for breast cancer recurrence prediction, following PRISMA guidelines. Peer-reviewed studies published between 2017 and 2025 were systematically identified, screened, and categorized based on learning paradigm, data modality, prediction objective, and evaluation strategy. The review provides a structured synthesis of methodological trends, comparative performance outcomes, and clinical applicability across machine learning, deep learning, hybrid, and ensemble frameworks. Key challenges related to data heterogeneity, class imbalance, interpretability, longitudinal modeling, and external validation are critically analyzed. The findings highlight hybrid and ensemble approaches as the most promising solutions for robust recurrence prediction. Future research directions emphasize explainable, survival-aware, and clinically deployable AI models for precision oncology. Keywords : Breast Cancer Recurrence; Machine Learning; Deep Learning; Hybrid and Ensemble Models Full Text : Download PDF DOI : 10.65809/IJAITE/24/v09i01/001 Cite this paper : - References : [1] Monisha, J. A., Sarathi, S., Hemavathy, V., Pandian, B., & Nightingale, F. (2023). Management of Breast Cancer: an Overview. CARDIOMETRY, 26, 589–592. https://doi.org/10.18137/cardiometry.2023.26.589592 [2] S. Hugar and S. V, "Early Detection of Breast Cancer Recurrence using Machine Learning for Improved Patient Outcomes," 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bangalore, India, 2025, pp. 1-6, doi: 10.1109/IITCEE64140.2025.10915526 [3] Acs, G., Esposito, N., Kiluk, J. V., Laronga, C., Lee, M. C., Loftus, L., Soliman, H., Boughey, J. C., Reynolds, C., Acs, P., & Gordan, L. (2009). 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