In today's data-driven landscape, characterized by massive data volumes, efficient query processing within Big Data environments has become crucial. This paper tackles significant challenges related to scalability and performance optimization in query processing. As datasets grow exponentially, the need for robust solutions that can manage this flood of data while ensuring accurate and timely results is critical. This study undertakes a thorough examination, beginning with a detailed review of the literature and existing techniques, and concluding with the presentation of a comprehensive approach. This method combines meticulous data preprocessing, the integration of advanced query processing techniques, and the implementation of scalability measures. Additionally, the paper explores various performance optimization strategies, including advanced indexing mechanisms, parallel processing models, and smart caching techniques. Through extensive experimental evaluations across diverse datasets, we outline the clear advantages of our proposed methods, demonstrating notable improvements in both scalability and query processing times. These findings underscore not only theoretical advancements in Big Data query processing but also the practical relevance of our approach in real-world scenarios. By offering valuable insights and empirical evidence, this paper provides both academic scholars and industry experts with essential guidance for navigating the complex landscape of Big Data analytics and processing.