Cloud computing has revolutionized the field of distributed systems through flexible, scalable, and cost-effective infrastructure. In spite of that, resource optimization in dynamic, heterogeneous environments remains a bottleneck. The traditional methods, which perform exceptionally well in static contexts, succumb to the multi-objective and complex nature of cloud computing-based systems. In this context, a class of algorithms derived through inspiration from the principles of natural selection has emerged as one of the robust alternatives: the Evolutionary Algorithms. This paper discusses the capabilities of Evolutionary Algorithms (EAs), namely Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and DE, for solving some of the most important challenges in cloud resource management. In the performed study, main optimizations were focused on three key areas: dynamic task scheduling, load balancing, and cost-efficiency. Very recent developments also include hybrid models that include EA with machine learning techniques, which have been discussed here for their role in scalability and adaptability. The experimental results, obtained by performing simulations under standardized benchmarks, show the superior performance of EAs when dealing with multi-objective problems compared to those obtained by traditional methods. The results contribute to the growing pool of evidence on applying EAs in cloud environments and provide insight into their effectiveness and further areas for improvement. This work lays the foundation for advancements in cloud resource optimization by leveraging principles of evolution toward meeting evolving computational demands.