Resource scalability and elasticity over cloud data centers have become crucial aspects in the cloud environment necessitating the prediction of the future workload over the servers. However, the dynamically fluctuating resource demands along with the redundant and noisy clients’ requests, further makes the attainment of the accurate predictions more challenging- a gap that has been left unaddressed by the existing approaches, further lacking the adaptability to the workload spikes in the real-time scenarios. This research paper aims upon formulating an adaptive workload forecasting approach using Particle Swarm Optimization-enhanced autoencoder developed to adaptively learn from the workload traces using the historical dataset. By utilizing the benchmark dataset, Bitbrains for the performance evaluation, the model’s efficacy has been computed in terms of root mean square error (RMSE), mean absolute error (MAE), and R-squared score (R2). The simulation results revealed that the PSO-optimized neural model outperformed various traditional approaches by reducing the RMSE score and MAE score by approximately 75% and 76%, respectively. In addition to that, the proposed model attained significant improvement in the R2 score nearing to 0.96 marking the significant improvement in the prediction accuracy when compared to the contemporary workload forecasting approaches; henceforth, offering the robust solution to perform workload prediction in the dynamic cloud environment.