One way to fix the fact that current methods aren’t accurate or efficient is to make a cloud-based load forecasting system that uses better types of deep learning. The pre-processed data is originally divided into many groups with almost identical volume distributions. Outliers in the data may be identified more easily this way. To analyze data that deviates from the norm across all domains, we employ a method known as spark-based density peak clustering. After the procedure is completed, the distinct clusters are reconnected with the remainder of the group. The parallel processing capabilities of the Spark platform for cluster computing allow for the concurrent execution of several operations in the data processing pipeline. Platform-level incompatibilities have hindered the widespread adoption of the cloud model approach and clashed with incredible tectonic force. This has enraged many individuals. This is analogous to how smart computers currently are, but, due to this difficulty, mainstream acceptance has proven difficult. Even though working in the cloud has its challenges, it shouldn’t come as a surprise that computers are so complicated. Future loads can be forecasted using a recurrent unit network model. One way to fix the fact that current methods aren’t accurate or efficient is to make a cloud-based load forecasting system that uses better types of deep learning. For a parallel program to work successfully, each component’s memory, computational capacity, and connections must be enough. When developing software for a parallel computer, it is critical to under-stand the memory structure of the architecture and how it will be distributed among the various cores. This is due to the fact that how effectively memory is structured and handled has a significant impact on how quickly programs run. Experiments show that the proposed strategy may minimize the mean forecast error by up to 0.023 MW in the short run, 19.75% in the medium run, and 2.76% in the long run. Based on the experimental results, these values were computed. The compared results outperform those obtained by other means, and the parallel performance is of sufficient quality to suggest that the methodology might be used.