This paper presents an enhanced method for improving the performance of cloud computing task scheduling using optimization strategies. The goal is to optimize task scheduling in cloud environments by reducing Total Execution Cost (TEC), Total Execution Time (TET), Energy Consumption (EC), and Response Time (RT). The proposed method integrates three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W), forming the TBW optimization approach. Experimental results show that TBW outperforms existing methods like GA-PSO and Whale Optimization in achieving the targeted objectives. The study highlights the importance of efficient resource usage and system effectiveness, particularly in the range of 8 to 14 virtual machines (VMs), where TBW achieves a 95% improvement in these areas. Cloud computing enables scalable and affordable computing resources, but efficient task scheduling is crucial for maximizing resource usage and enhancing cloud service performance. Scientific workflows, such as MONTAGE, LIGO, SIPHT, and GENOME, involve millions of tasks and require efficient mapping to cloud resources. Numerous approaches exist for discovering optimal schedules, with metaheuristics being widely used to address complex optimization problems like TET, energy consumption, and response time. Heuristic and metaheuristic approaches are frequently used in cloud computing to tackle scheduling, resource allocation, and other complex problems. These methods balance solution quality and computational efficiency, making them suitable for real-time cloud implementations. They are effective in addressing issues such as cloud environment complexity, time constraints, resource optimization, scalability, and adaptability to uncertainty. The paper discusses various scheduling criteria in cloud environments, including factors like TEC, TET, EC, and RT, which are critical for optimizing task scheduling. The proposed TBW optimization method aims to improve these criteria through the integration of three optimization techniques.This paper presents an enhanced method for improving the performance of cloud computing task scheduling using optimization strategies. The goal is to optimize task scheduling in cloud environments by reducing Total Execution Cost (TEC), Total Execution Time (TET), Energy Consumption (EC), and Response Time (RT). The proposed method integrates three optimization techniques: Tabu Search (T), Bayesian Classification (B), and Whale Optimization (W), forming the TBW optimization approach. Experimental results show that TBW outperforms existing methods like GA-PSO and Whale Optimization in achieving the targeted objectives. The study highlights the importance of efficient resource usage and system effectiveness, particularly in the range of 8 to 14 virtual machines (VMs), where TBW achieves a 95% improvement in these areas. Cloud computing enables scalable and affordable computing resources, but efficient task scheduling is crucial for maximizing resource usage and enhancing cloud service performance. Scientific workflows, such as MONTAGE, LIGO, SIPHT, and GENOME, involve millions of tasks and require efficient mapping to cloud resources. Numerous approaches exist for discovering optimal schedules, with metaheuristics being widely used to address complex optimization problems like TET, energy consumption, and response time. Heuristic and metaheuristic approaches are frequently used in cloud computing to tackle scheduling, resource allocation, and other complex problems. These methods balance solution quality and computational efficiency, making them suitable for real-time cloud implementations. They are effective in addressing issues such as cloud environment complexity, time constraints, resource optimization, scalability, and adaptability to uncertainty. The paper discusses various scheduling criteria in cloud environments, including factors like TEC, TET, EC, and RT, which are critical for optimizing task scheduling. The proposed TBW optimization method aims to improve these criteria through the integration of three optimization techniques.