In order to classify the Cleveland Heart Disease dataset, this study evaluates the performance of three optimization methods, namely Fruit Fly Optimization (FFO), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The top 10 features are identified using FFO, with remarkable results for accuracy (88.52%), precision (87.88%), recall (90.63%), and f1-score (89.23%). After using PSO, the accuracy, precision, recall, specificity, and f1-score are all 85.25%, 87.10%, 84.38%, and 85.71% respectively. Finally, GWO is used, which results in precision, accuracy, recall, and f1-score values of 93.33%, 90.16%, 90.11%, 87.5%, and 90.32% respectively, highlighting its consistent superior performance. FFO shows competitive outcomes with notable accuracy and recall through a comparative examination. PSO displays comparable precision and recall while displaying a somewhat poorer accuracy. In contrast, GWO performs better than both FFO and PSO, displaying great accuracy and precision along with remarkable recall and specificity. These results provide important information on the effectiveness of feature selection methods utilized in optimization algorithms for heart disease classification. The study also highlights the need for more investigation into the potential of these optimization algorithms in many fields, broadening their use beyond the classification of disease. This kind of work might improve the progress of the field of feature selection and aid in the creation of better classification models.