As in the case of software used in the diagnosis of heart diseases based on artificial intelligence (AI), the efficiency of any model greatly depends on the quality and usefulness of the characteristics on which it works. The importance of feature selection as a procedure of selecting all the most meaningful variables in input, whilst rejecting others, cannot be overstated in terms of its contribution to the accuracy and efficiency of predictive algorithms. Removing the irrelevant, redundant, or noisy features, models will be better able to generalize, compute faster, and be more interpretable. In this chapter, the authors investigate the use of metaheuristic optimization after applying the technique to the feature selection of cardiovascular data. Metaheuristics, based on naturally occurring processes and evolutionary approaches, supply a strong framework for searching the large and complicated feature space to distinguish ideal sets. The most famous ones, like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), are investigated in more detail along with their mathematical representation, in how they work, and perform in real life in diagnosing the cases. Comparative studies between metaheuristic methods and classical feature selection methods, including filter-type and wrapper-type methods, were also provided in the chapter. The exigencies of metaheuristics over conventional methods and approaches are proven in terms of classifier accuracy, dimensionality reductions, and cost in time when experimented on about benchmark cardiovascular data through case studies with experimental results. The results emphasize the usefulness of such biologically inspired algorithms as the necessary sources of high-performing AI systems to predict and diagnose heart diseases.