Reliable data transmission is essential for next-generation communication systems such as 5G, 6G, IoT, satellite communications, and intelligent edge networks. Conventional Error Correction Codes (ECC) such as LDPC and Polar codes provide excellent error resilience but often rely on computationally intensive decoding algorithms that are difficult to adapt to rapidly changing wireless channel conditions. Machine Learning (ML) offers a data-driven alternative by learning decoding patterns directly from received noisy signals. This paper proposes a machine learning-assisted error correction framework that integrates a supervised learning model with a conventional ECC communication system to improve decoding accuracy while reducing latency and computational complexity. The framework considers AWGN and Rayleigh fading channels and evaluates performance using Bit Error Rate (BER), Frame Error Rate (FER), accuracy, precision, recall, F1-score, and decoding time. The proposed framework is intended as a scalable solution for future intelligent communication systems and provides a foundation for integrating AI-based decoding into next-generation wireless networks.

Keywords
Error Correction Codes (ECC), Machine Learning, LDPC Codes, Channel Decoding, BER, Wireless Communications, 6G Networks, Artificial Neural Networks, IoT, Adaptive Decoding.