The rapid and significant increase in the volume of big data has made it necessary to develop robust security protocols in order to protect confidential data from unauthorized access and electronic threats. This material examines advanced encryption techniques developed specifically to efficiently maintain large-scale data sets. We are addressing the distinct difficulties posed by large data settings, which include aspects of size, frequency, and diversity. We have developed a complete strategy that includes several modern encryption techniques, the code described as the perverted ECC, the homomorphic encryption, the lattice-based coding, and the quadratic code (MQ) encoding. By integrating these methods, we have a hybrid encryption system that effectively manages both performance and security. The efficiency of the system has been demonstrated through comprehensive performance studies, which show minimum accounting overheads and significant accuracy in data recovery. Security assessments of the system have verified its ability to withstand brute force attacks, frequency analysis, and future threats such as quantitative computerization. The real-world case studies show the resilience and efficiency of the system in several sectors, such as health care, banking, and e-commerce. The study provides a practical approach to improving the security of large-scale data, ensuring the accuracy and reliability of data and protecting individual privacy. The areas that could be studied in the future were further refinement and integration of future technologies such as mass chains and artificial intelligence.