This paper presents a comprehensive analysis and suggests a big data-driven system architecture designed to enhance code quality and improve development efficiency. Utilizing the extensive data produced throughout the software development lifecycle, the system employs sophisticated analytical methods like data mining, machine learning, predictive modeling, and Natural Language Processing (NLP). The suggested architecture enables real-time defect detection, anomaly identification, code quality prediction, and intelligent suggestions for testing and refactoring. It incorporates scalable technologies like as Apache Kafka, HDFS, Apache Spark, and contemporary visualization tools to provide rapid data intake, distributed processing, and actionable insights. The document examines significant implementation obstacles, such as data integration, privacy issues, and computing overhead, while providing practical recommendations for effective deployment. Case studies of firms such as Microsoft and Google are analyzed to illustrate the concrete effects of big data analytics on software quality assurance. The research also addresses future developments, including integration with Agile and DevOps processes, cloud scalability, and edge computing. This study offers a complete technique for enhancing software quality assurance by integrating theoretical underpinnings with practical system design and using big data.