<p>This research introduces a hybrid time synchronization approach and a machine learning (ML)-based M-ary Quadrature Amplitude Modulation (M-QAM) demodulator to enhance performance in Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) systems under challenging conditions. The proposed hybrid time synchronization technique leverages an Artificial Neural Network (ANN) to optimize synchronization sequences, improving robustness in both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. Compared to previous methods, the ANN-powered synchronization achieves a higher correlation peak-to-sidelobe ratio, enabling dynamic adaptation to varying channel conditions and significantly enhancing symbol detection accuracy. In the second part of the study, machine learning-based M-QAM demodulators are developed to mitigate demodulator impairments in OFDM and MIMO-OFDM systems. The demodulators, trained using ANN, Kernel Support Vector Machine (KSVM), and Linear Discriminant Analysis (LDA) models, demonstrate superior performance in terms of Bit Error Rate (BER) under various demodulator impairments. The ANN-based demodulator not only achieves near-optimal BER under impairments but also outperforms the other ML-based demodulators as well as the conventional demodulator in terms of computational efficiency, offering reduced processing times and latency, particularly at high modulation orders. The results suggest that the ANN-based demodulator, combined with the proposed hybrid synchronization method, could replace existing solutions in digital communication systems, providing enhanced performance, especially under challenging conditions.</p>