This paper presents a novel approach to investigate gait alterations through the utilization of mobile devices and Artificial Intelligence. The study delves into the current state of the art in gait analysis, reviewing existing methodologies and advancements. This work aims to detect and monitor gait abnormalities using reachable mobile devices. The methodology involves machine learning algorithms to analyze data collected from accelerometers and gyroscopes in mobile devices. This approach enables real-time tracking and assessment of gait patterns, providing a comprehensive understanding of alterations that may indicate underlying health conditions. The paper contributes to the existing body of research by introducing a cost-effective and accessible solution for continuous gait monitoring. The utilization of mobile devices not only enhances the convenience of data collection but also facilitates remote monitoring, making it a valuable tool for both clinical and research applications. In conclusion, this work discusses the potential implications of our proposed methodology in complementing and advancing existing gait analysis studies. The integration of mobile devices and machine learning holds promise for revolutionizing gait assessment, paving the way for new insights into health conditions and contributing to the evolution of rehabilitation and diagnostic practices.