These days, machine learning (ML) is one of the most important technologies, especially in the field of healthcare. Alongside the rapid growth of high-quality medical data and information, its importance is growing. Even with these advancements, early and accurate disease identification is still a difficult task. ML models are trained on historical healthcare data to learn patterns and relationships between different variables. Once trained, these models can make predictions about future events or outcomes, such as the likelihood of a patient developing a particular disease, the risk of complications, or the effectiveness of treatment. Based on the unique traits and medical background of each patient, ML models can forecast the likelihood that a patient would suffer from a certain disease or have a negative health event. For individuals who are more likely to require therapy, this data may be utilized to tailor treatment regimens, assign resources, and prioritize treatment. In this study, ML has been used to support healthcare prediction through the use of two models: Extra Trees Classification (ETC) and Support Vector Classification (SVC). To improve the accuracy of the findings derived from the models, two optimizers Smell Agent Optimization (SAO) and Crocodile Hunting Strategy (CHS) used in combination with two models, SVC, ETC. Four models ETSA, ETCH, SVSA, and SVCH were developed in this study by integrating the ETC and SVC models with two optimization algorithms the CHS and SAO. Upon analyzing the performance of these models, it was found that the ETSA model had a high precision of 0.806 in normal conditions.