The practice of cycling is widely acknowledged for its substantial contributions to both individual health and environmental sustainability, as it not only enhances cardiovascular fitness levels but also plays a critical role in diminishing the release of carbon emissions into the atmosphere. Nevertheless, despite these numerous advantages associated with cycling, individuals who engage in this activity face a variety of specific health challenges, which include musculoskeletal injuries stemming from repetitive strain, severe traumas to the lower extremities due to traffic collisions, and a range of more insidious neurological complications that may manifest over time. Traditional methods of diagnosis, which typically encompass physical examinations, imaging studies, and the subjective reporting of symptoms by patients, frequently prove inadequate when it comes to providing timely and precise insights into these diverse and multifaceted medical conditions. However, with the recent advancements in the fields of Artificial Intelligence (AI), machine learning (ML), and the integration of multi-modal data, automated diagnostic systems are increasingly emerging as a transformative force within the realm of healthcare delivery. This article aims to present an extensive review of the various diseases related to cycling and to explore the ways in which automation can effectively address the limitations associated with traditional diagnostic methods while simultaneously fostering safer and more effective cycling practices.