Load balancing in cloud computing is essential for optimizing resource utilization and enhancing system performance. This paper explores the strengths and weaknesses of clustering and classification algorithms in the context of load balancing. It provides a detailed comparative analysis of these two approaches across various contexts relevant to cloud computing. The paper then investigates integration strategies that combine clustering and classification methods to leverage their complementary strengths. For each strategy, related work has been cited and discussed. Our findings reveal that hybrid methods, which integrate clustering and classification, offer significant advantages in adaptability and efficiency.