In advanced wireless communication, large-scale Multiple-Input Multiple-Output (Multiple-Input Multiple-Output (MIMO)MIMO) systems are pivotal in laying the foundation for 5G networks and future technologies. One of the primary challenges in Multiple-Input Multiple-Output (MIMO)MIMO systems lies in efficiently processing signals with one-bit analog-to-digital converters (ADCs)—a cost-effective yet technically constrained approach. This chapter introduces novel Maximum Likelihood (ML) detection methods grounded in machine learning to enhance Multiple-Input Multiple-Output (MIMO)performanceMIMO performance under 1-bit quantization constraints. This work has been introduced by Choi et al. [1]. Two innovative ML-based detection methods are proposed in [1]: the bias learning method and the dithering learning method. Both approaches aim to improve signal detection performance in 1-bit quantization scenarios, while notably not requiring CSI. Additionally, this study presents a post-processing update technique for likelihood functions that leverages information from accurately decoded symbols as part of the learning mechanism. Simulation results demonstrate that the proposed methods substantially reduce signal error rates in comparison with conventional techniques. These findings mark a significant advancement toward the development of efficient, low-cost Multiple-Input Multiple-Output (MIMO)MIMO systems suited to meet the escalating demands of energy-efficient wireless applications.