The use of high-performance computing (HPC) infrastructure in a cloud computing environment is an effective approach for running data-intensive applications. The MapReduce (MR) framework is a parallel computing solution that is commonly used for high-performance applications that involve the analysis of BigData, scientific research, and data-intensive tasks. Hadoop is a parallel computing framework based on MapReduce that enjoys widespread adoption among diverse organizations. The Apache foundation offers an open source framework that can be obtained at no cost. The current makespan methodology that employs Hadoop MapReduce (HMR) results in memory and I/O overhead, which has an adverse effect on the makespan performance. The proposed publication presents a model called Hybrid HMR (HHMR) makespan, which aims to effectively tackle research problems and obstacles. The Hybrid Hadoop MapReduce (HHMR) technique utilizes virtual computing workers to execute tasks in parallel, resulting in reduced makespan times.