In the digital era, all medical records are managed and kept digitally. The detailed amount of memory storage space needed to store medical data is growing rapidly as the population grows. The storage facilities where the patient records are kept cost a lot of money to the healthcare and pharmaceutical industries. As the data must be fully recovered during decompression, traditional lossy storage strategies are unable to be utilized to store medical data. To store medical data quickly and effectively, this research introduces a revolutionary lossless data compression technique. A dictionary-based lossless compression method namely Lempel–Ziv–Welch (LZW) compression uses data redundancy to reduce its size. When needed, compressed information can be rapidly and completely retrieved. Other lossless compression methods are contrasted with the suggested approach. Python is the language used to implement the recommended approach, and High Availability Distributed Object Oriented Platform (HADOOP) is applied to store the data that has been compressed.