Cloud computing is a fundamental paradigm for computing services based on the elasticity attribute, in which available resources are effectively adjusted for changing workloads over time. One of the most important challenges in such systems is the task scheduling problem, which aims to identify the optimal resource allocation to maximize performance and minimize response times. To get around these restrictions, the new Dynamic BIRCH based BIGRU model for time series prediction (DynaBit) technique is suggested for future server load prediction. Predicting time series, collecting workloads, preprocessing and clustering them, and post-processing the data are all steps in the suggested approach. The workload data will be divided according to a historical time window during the preprocessing phase. The time series data will then be clustered based on the latency classes using the Dynamic Birch algorithm. The original data is recovered through postprocessing, and the Bidirectional Gated Recurrent Unit (BIGRU) is employed in the time series prediction phase. The proposed model has been compared with previous approaches involving Parallel Algorithm, HEFT and FCFS approaches in terms of prediction accuracy by 31.9%, 18.74%, and 12.16%, respectively.