Heart disease is a significant public health concern globally, and timely and accurate diagnosis is critical for effective treatment. Data classification techniques have been extensively used to identify potential heart disease patients using various medical data attributes such as age, blood pressure, cholesterol levels, and family history. However, the performance of these classification algorithms largely depends on the selection of appropriate features and the tuning of algorithm parameters. Hybrid optimization techniques offer a promising solution to improve the performance of heart disease classification models. These techniques combine the advantages of multiple optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing, to overcome their individual limitations and achieve optimal results. The hybrid optimization technique can be used to optimize feature selection and hyperparameter tuning, resulting in improved classification accuracy and reduced computational time. Moreover, hybrid optimization techniques can handle complex data distributions, which is particularly relevant in the case of heart disease diagnosis, where data patterns can be highly nonlinear. In summary, the application of hybrid optimization techniques can significantly improve heart disease classification models' accuracy, making them more effective in identifying potential patients and aiding in timely medical interventions.