In today’s digital world, the effectiveness of data communication depends on the seamless and secure exchange of information. Encryption, paired with compression, is used to ensure secure and faster communication. These techniques ultimately save network bandwidth as well as storage space, while safeguarding the user’s data. In comparison with traditional systems, the proposed system provides higher throughput, minimizes latency, and enhances computation by using parallel computing technologies like CUDA and OpenCL. This research work includes parallel and multi-threaded implementation of data compression algorithms like Huffman and K-means and data encryption via the advanced encryption standard (AES) algorithm. The primary goal of the proposed system is to compress and encrypt data using graphical processing units (GPUs), which makes the system time, space, and energy efficient. The secondary focus of the proposed system is cross-platform portability and hardware independence, which permits the parallel algorithms to run on all desktop operating systems and compute devices like CPUs and GPUs, ranging from embedded devices to server workstations. This research targets data compression and encryption at a larger scale with respect to data size. Performance benchmarks show that the proposed CUDA implementation can run up to 15 × better than traditional CPU-only systems for compression with encryption and 17 × better for decompression with decryption. Similarly, the OpenCL implementation runs 13 × better for compression with encryption and 15 × better for decompression with decryption.