In the era of big data, efficiently mining high utility patterns from massive datasets is a critical challenge. It has applications in various domains such as retail, healthcare, finance, and more. Novel algorithms and techniques are proposed to address the challenges posed by high utility pattern mining for big data applications. A novel high utility mining model that combines High Utility Upper Bound Pruning, the High Utility Prefix Tree, and HUP-MapReduce Parallel Processing is designed. This approach focuses on improving the efficiency and scalability of high utility mining algorithms, enabling them to handle massive datasets with high-dimensional itemsets. The proposed algorithms are evaluated using real-world retail dataset, showcasing the effectiveness in discovering valuable patterns from big data. The proposed model is designed to address the scalability and runtime limitations of existing algorithms. Through a comprehensive experimental evaluation, the superior efficiency of the proposed model is demonstrated. Proposed model consistently outperforms achieving an average reduction of 0.01% in runtime while generating lesser number of candidate patterns. This efficiency of proposed approach, positions it as a powerful solution for mining high utility patterns from big data.