Mobile edge computing and Internet of Things (IoT) become most general in both private as well as public sectors, playing a progressively crucial role in medical applications. However, they focus on the delay caused by encryption and decryption in transmitting sensitive medical data, especially in systems that need to process large volumes of data quickly. Hence, this research proposes the Tent Chaotic Mapping-Based Zebra Optimization Algorithm (TCM-ZOA) approach for the feature selection process in the collected healthcare medical data. This helps reduce the amount of data to be transmitted by eliminating irrelevant or redundant features. This directly addresses the issue of time consumption in encryption and decryption by reducing data size. The min–max normalization technique is performed in collected healthcare data to normalize the input data. The cryptographic approach of Blowfish is utilized to performing encryption as well as decryption procedure, which provides a greater degree of security against attacks. The experimental results demonstrate that the proposed TCM-ZOA approach attains the better accuracy of 98.39% and encryption time of 0.203 s respectively as compared to the existing methods like ZOA and two fish.