This research focuses on enhancing the performance of cloud computing task scheduling using optimization strategies. The authors aim to develop and evaluate methods that can reduce the Total Execution Cost (TEC), Total Execution Time (TET), Energy Consumption (EC), and Response Time (RT) in cloud environments. They propose a TBW (Tabu Search, Bayesian Classification, and Whale Optimization) methodology, which outperforms other well-known approaches like GA-PSO and Whale Optimization. The study emphasizes the importance of efficient resource usage and system effectiveness, achieving a 95% improvement for the range of 8 to 14 VMs. The research is significant for improving the overall performance of cloud computing systems, making them more scalable and efficient.This research focuses on enhancing the performance of cloud computing task scheduling using optimization strategies. The authors aim to develop and evaluate methods that can reduce the Total Execution Cost (TEC), Total Execution Time (TET), Energy Consumption (EC), and Response Time (RT) in cloud environments. They propose a TBW (Tabu Search, Bayesian Classification, and Whale Optimization) methodology, which outperforms other well-known approaches like GA-PSO and Whale Optimization. The study emphasizes the importance of efficient resource usage and system effectiveness, achieving a 95% improvement for the range of 8 to 14 VMs. The research is significant for improving the overall performance of cloud computing systems, making them more scalable and efficient.