Social media platforms operate at top speeds when transferring image-based data. The shared and posted images and videos on WhatsApp and Instagram consume the majority of network resources. Lossless compression techniques were applied to images while maintaining image quality throughout data storage and transmission processes because this fundamental method produces perfect information reconstruction after decompression. The research evaluates Predictive Coding (DPCM) and Context-Based Coding and Arithmetic Coding and Dictionary-Based Techniques (LZW) and Block-Based Compression through analyses of their efficiency metrics and computational complexity and practical usage. New developments in JPEG2000 and LZW compression have led to increase speed and efficiency through Parallel Symbol Encoding in Arithmetic Coding and Compression Ratio Prediction. The Optimized Run-Length Encoding (ORLE) system uses dynamic compression approach adaptation according to image orientation to enhance its flexibility. The speed of real-time applications increases remarkably when using FPGA implementations. This survey examines trade-offs among compression ratio together with computational expense and suitable data sets to perform an evaluation between classical and modern methods. Future development in lossless data and image compression relies on emerging trends such as AI-driven compression models as well as hardware-accelerated algorithms and hybrid frameworks Deflate is the fastest compression technique taking about 0.043 s, with a Maximum compression ratio of 26.66 given by WebP.