This review explores the transformative impact of Machine Learning (ML) on managing diabetic foot complications, highlighting its potential to enhance diagnostic accuracy and therapeutic approaches. The authors conducted a meticulous literature search across major databases, focusing on ML applications in medical imaging, biomarker detection, and clinical biomechanics. ML has proven invaluable in deriving critical insights from complex datasets, enhancing diagnostic precision and therapeutic planning. Key contributions include: 1. **Medical Imaging**: ML algorithms have improved the accuracy of diabetic foot wound assessment, particularly through infrared thermography, which can detect early signs of complications. 2. **Biomarker Detection**: ML has been instrumental in identifying potential biomarkers for early and precise diagnosis, such as metabolomic changes and genetic markers. 3. **Clinical Biomechanics**: ML models have accurately diagnosed diabetic peripheral neuropathy and predicted wound healing outcomes, offering personalized treatment strategies. The review also discusses the use of ML in prognostic assessments, including predicting ulcer healing, amputation risk, and mortality. Studies have shown that ML models can provide more accurate and timely predictions compared to traditional methods, facilitating early interventions and personalized care. Additionally, the review compares various ML algorithms, evaluating their performance in different clinical studies. For example, the Light Gradient Boosting Machine (XGBoost) has demonstrated superior performance in predicting amputation risks, while Support Vector Machines (SVM) have excelled in wound boundary detection. The importance of diverse datasets, such as STANDUP, INAOE, and Zivot, is emphasized, as they provide valuable insights for developing and validating predictive models. These datasets, combined with advanced imaging tools, enhance the precision of ML applications in diabetic foot care. Overall, the review underscores the critical role of ML in advancing diabetic foot care, offering new avenues for research and treatment modalities. Despite challenges like computational limitations and ethical considerations, the future of ML in diabetic foot care is promising.This review explores the transformative impact of Machine Learning (ML) on managing diabetic foot complications, highlighting its potential to enhance diagnostic accuracy and therapeutic approaches. The authors conducted a meticulous literature search across major databases, focusing on ML applications in medical imaging, biomarker detection, and clinical biomechanics. ML has proven invaluable in deriving critical insights from complex datasets, enhancing diagnostic precision and therapeutic planning. Key contributions include: 1. **Medical Imaging**: ML algorithms have improved the accuracy of diabetic foot wound assessment, particularly through infrared thermography, which can detect early signs of complications. 2. **Biomarker Detection**: ML has been instrumental in identifying potential biomarkers for early and precise diagnosis, such as metabolomic changes and genetic markers. 3. **Clinical Biomechanics**: ML models have accurately diagnosed diabetic peripheral neuropathy and predicted wound healing outcomes, offering personalized treatment strategies. The review also discusses the use of ML in prognostic assessments, including predicting ulcer healing, amputation risk, and mortality. Studies have shown that ML models can provide more accurate and timely predictions compared to traditional methods, facilitating early interventions and personalized care. Additionally, the review compares various ML algorithms, evaluating their performance in different clinical studies. For example, the Light Gradient Boosting Machine (XGBoost) has demonstrated superior performance in predicting amputation risks, while Support Vector Machines (SVM) have excelled in wound boundary detection. The importance of diverse datasets, such as STANDUP, INAOE, and Zivot, is emphasized, as they provide valuable insights for developing and validating predictive models. These datasets, combined with advanced imaging tools, enhance the precision of ML applications in diabetic foot care. Overall, the review underscores the critical role of ML in advancing diabetic foot care, offering new avenues for research and treatment modalities. Despite challenges like computational limitations and ethical considerations, the future of ML in diabetic foot care is promising.