aspg-3-3453-1791417679 20261008000119 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 18 1 Sampath Kini K. Sampath Kini K, Assistant Professor, Computer Science and Engineering, NITTE Deemed to be University, Karnataka, India D. K. Sreekantha Professor, Computer Science and Engineering, NMAM Institute of Technology, NITTE Deemed to be University, Karnataka, India This research illustrates how dynamic task balancing and data sharing may improve distributed data processing. The technology handles parallel processing system difficulties with huge datasets by minimizing resource utilization, time complexity, and output. We modify the workload on the fly after splitting to ensure that all processing units receive equal work. One last optimization phase optimizes job distribution to maximize system efficiency. We test the solution for latency, speed, scalability, resource utilization, fault tolerance, and synchronization overhead. Results reveal that the new strategy outperforms existing ones in every regard. It features the lowest latency, quickest production, and highest growth potential. The approach handles mistakes well, divides data effectively, and syncs everything at a cheap cost. These properties make it ideal for real-time data processing and fast-growing applications. Future study will concentrate on flexible splitting strategies, fault tolerance mechanisms, and predictive analytics machine learning models. These modifications will improve real-time data handling. 2025 104 115 3453 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.180109 https://www.americaspg.com/journal/3/article/3453 https://www.americaspg.com/storage/11735528958.pdf