In today\\\'s digital era, ensuring the confidentiality, integrity, verifying users and entities, and maintaining the secrecy of sensitive information of transmission are crucial; both in terms of speed and memory use is a growing challenge, especially for resource-limited devices like IoT, mobile, and embedded systems. The thesis presents an entropy-aware Key Encapsulation Mechanism (KEM) and Data Encapsulation Mechanism (DEM) hybrid scheme. The technical design pairs AES-256 (CBC mode) for bulk data confidentiality with ECC (secp256r1) for secure key encapsulation, using SHA 256 for integrity verification where needed. Performance benchmarking was conducted on an HP laptop (Intel i7 7200U) using distinct datasets: Audio and Image partitioned in 10% increments to analyse allocation effects. The research provides an empirically validated framework that offers specific engineering guidelines for optimising memory and latency in secure data transmission. The inclusion of entropy adaptation combined with NIST test vectors (Known Answer Tests) to validate the correctness of the hybrid implementation, distinguishing it from prior works that often lacked validation. These innovations enhance cryptographic performance for IoT and edge computing environments where both resource efficiency and strong security are imperative. The study recognises limitations in hardware diversity, sector-specific applicability, and exclusion of PQC showing directions for future studies.