Machine learning (ML) is playing a transformative role in the management of diabetic foot (DF) complications, offering new opportunities for improving diagnostic accuracy, therapeutic planning, and patient outcomes. This review explores the application of ML in DF care, focusing on its potential to enhance diagnostic precision, prognostic accuracy, and the overall management of DF. The review highlights the integration of ML with medical imaging, biomarker detection, and clinical biomechanics, demonstrating its capacity to revolutionize DF care through advanced computational methods. DF is a complex complication of diabetes mellitus, characterized by neuropathy, peripheral arterial occlusive disease, and infection. It affects approximately 18.6 million individuals annually, with a majority progressing to foot ulcers, leading to high amputation rates. Traditional diagnostic and therapeutic methods face limitations, particularly in early diagnosis. The integration of high-throughput genomics and advanced imaging technologies has expanded DF research into an interdisciplinary field. However, the current state of interdisciplinary collaboration remains insufficient to address the multifaceted nature of DF. The influx of multimodal data from diverse disciplines introduces additional complexities in data analysis, necessitating the use of sophisticated analytical tools such as ML and artificial intelligence. ML has evolved from a peripheral technology to a cornerstone in medical data analytics, with transformative impacts across various medical disciplines. In clinical practice, ML techniques can mine key information from large amounts of medical data to provide more accurate diagnosis, prediction, and treatment recommendations. ML algorithms have proven to be invaluable in handling specialized data types, such as single-cell RNA sequencing medical imaging and multi-omics data integration. ML has revolutionized medical research and practice by autonomously "discovering" and optimizing algorithms to solve specific problems. Emerging evidence underscores the instrumental role of ML in advancing DF research. ML algorithms enable clinicians to leverage biomarkers for the early diagnosis of DF, initiating timely interventions. ML has been employed to predict the healing trajectory of DF ulcers, assess amputation risks, and formulate personalized treatment regimens. Recent studies have explored the use of ML in classifying thermal images of diabetic feet for early detection of complications. These applications not only deepen our understanding of the pathophysiological underpinnings of DF but also herald new avenues for future research and treatment modalities. The review systematically searched PubMed, Scopus, and Google Scholar up to September 2023, utilizing a detailed strategy with keywords focused on machine learning and diabetic foot pathology. The review emphasizes the critical evaluation of machine learning principles and their application to diabetic foot care, supported by case studies and diagrams for clarity. The review highlights the integral role of ML in enhancing diagnostic precision, prognostic accuracy, and the overall management of DF. It also explores the identification of emergent trends within this interdisciplinary sphere, spotlighting cutting-edge ML algorithms poised to significantly improve clinical outcomes. The review also discusses the challenges and limitations inherent in the current landscape, providing a balanced perspective to foster realistic expectations among healthcare practitioners and researchers.Machine learning (ML) is playing a transformative role in the management of diabetic foot (DF) complications, offering new opportunities for improving diagnostic accuracy, therapeutic planning, and patient outcomes. This review explores the application of ML in DF care, focusing on its potential to enhance diagnostic precision, prognostic accuracy, and the overall management of DF. The review highlights the integration of ML with medical imaging, biomarker detection, and clinical biomechanics, demonstrating its capacity to revolutionize DF care through advanced computational methods. DF is a complex complication of diabetes mellitus, characterized by neuropathy, peripheral arterial occlusive disease, and infection. It affects approximately 18.6 million individuals annually, with a majority progressing to foot ulcers, leading to high amputation rates. Traditional diagnostic and therapeutic methods face limitations, particularly in early diagnosis. The integration of high-throughput genomics and advanced imaging technologies has expanded DF research into an interdisciplinary field. However, the current state of interdisciplinary collaboration remains insufficient to address the multifaceted nature of DF. The influx of multimodal data from diverse disciplines introduces additional complexities in data analysis, necessitating the use of sophisticated analytical tools such as ML and artificial intelligence. ML has evolved from a peripheral technology to a cornerstone in medical data analytics, with transformative impacts across various medical disciplines. In clinical practice, ML techniques can mine key information from large amounts of medical data to provide more accurate diagnosis, prediction, and treatment recommendations. ML algorithms have proven to be invaluable in handling specialized data types, such as single-cell RNA sequencing medical imaging and multi-omics data integration. ML has revolutionized medical research and practice by autonomously "discovering" and optimizing algorithms to solve specific problems. Emerging evidence underscores the instrumental role of ML in advancing DF research. ML algorithms enable clinicians to leverage biomarkers for the early diagnosis of DF, initiating timely interventions. ML has been employed to predict the healing trajectory of DF ulcers, assess amputation risks, and formulate personalized treatment regimens. Recent studies have explored the use of ML in classifying thermal images of diabetic feet for early detection of complications. These applications not only deepen our understanding of the pathophysiological underpinnings of DF but also herald new avenues for future research and treatment modalities. The review systematically searched PubMed, Scopus, and Google Scholar up to September 2023, utilizing a detailed strategy with keywords focused on machine learning and diabetic foot pathology. The review emphasizes the critical evaluation of machine learning principles and their application to diabetic foot care, supported by case studies and diagrams for clarity. The review highlights the integral role of ML in enhancing diagnostic precision, prognostic accuracy, and the overall management of DF. It also explores the identification of emergent trends within this interdisciplinary sphere, spotlighting cutting-edge ML algorithms poised to significantly improve clinical outcomes. The review also discusses the challenges and limitations inherent in the current landscape, providing a balanced perspective to foster realistic expectations among healthcare practitioners and researchers.