Cloud computing technology gives users more scalability and flexibility by utilizing dynamic resources. Task scheduling and resource allocation are the two main issues in a cloud environment that directly affect system throughput and user satisfaction. The two main variables influencing the cloud system's performance are the amount of time needed to complete tasks and the computational cost. A multi-objective approach to task scheduling and resource allocation is presented in this work.In order to efficiently distribute computing resources and schedule tasks while minimizing time and cost objectives, the Enhanced Honey Badger algorithm (EHBA) is employed. Next, the algorithm is combined with LSTM to further optimize it.Time-to-execute, Cost-to-compute, Task-to-resource utilization, and Time-to-respond are some of the metrics used to evaluate how well the suggested EHBA method (in conjunction with LSTM) performs for effective task scheduling and resource allocation