Gait analysis is essential for the diagnosis of neuromuscular and musculoskeletal disorders. Traditional methods are vulnerable and lead to inconsistency as they rely on subjective assessments. An angle-based approach which uses advanced machine learning techniques have been used address this. Extracted joint angle measurements have been extracted from the video data using computer vision methods. The characteristics used in this research were used to train a hybrid model of a Random Forest classifier and a Fuzzy C-Means clustering algorithm. Random Forest model was used as it is stable and capable of dealing with intricate nonlinear relationships and Fuzzy C-Means was used as it can manage ambiguity in the data as well as overlapping class distributions. The results showed that the Random Forest classifier has a classification accuracy of 94.62%, which is better than the other models in distinguishing between normal and abnormal gait patterns. Fuzzy C-Means also shows high accuracy is capable of clustering various forms of gait and extracting detailed features in gait dynamics. Results suggest that integrating joint angle analysis with machine learning methods provides a credible tool for gait analysis, which can aid clinicians in the early detection and treatment of gait related disorders.