"Cloud computing" refers to internet-based computing on demand and describes an incredibly scalable technology used by working-class and non-working individuals globally. Fault-tolerant task scheduling is an essential tool used by end users and cloud suppliers. Finding the best resource for the specified input task presents a key challenge for fault-tolerant task schedulers. The studies that have already been done have attempted to address each of these complex issues independently. Still, it is tricky to optimize resources and provide fault tolerance at the same time. In this paper, an effective hybrid HBA-MAO and hybrid fault-tolerant mechanism in cloud computing are designed to appropriate task scheduling in VMs without delay and failure. Various tasks submitted by users and virtual machines are taken as input for the proposed approach. Hybrid Honey Badger Optimization Algorithm (HBA) and Mexican Axolotl Optimization (MAO) are used in this proposed for priority based optimal task scheduling. These scheduled tasks are assigned to the VM for execution. A fault-tolerant mechanism is immediately carried out if the tasks are not completed successfully. The hybrid reactive and proactive fault-tolerant mechanism is used in this proposed approach for a high level of fault tolerance. The proposed approach attains better performance, like 70 sec of response time, 13% of resource utilization and 95% success rate. This approach uses resources efficiently by reducing resource consumption, so it is the best choice for fault-tolerant aware task scheduling.