In order to accommodate the exponential growth of data-intensive apps and linked devices in the current era, the next generation of wireless networks must offer extraordinarily high speeds, great connection, and low latency. Two of the most significant advanced technologies fifth-generation (5G) networks use to fulfill these goals are multiple-input multiple-objectives (MIMO) and multiuser MIMO (MU-MIMO). The main emphasis of this work is on high-capacity communication and how MIMO and MU-MIMO technologies might enhance the performance of the 5G network. MU-MIMO expanded allows several users to access the same time-frequency resources free from interference, thereby optimizing spectrum consumption and boosting network capacity. These solutions meet the congested and dynamic conditions typical of modern urban and industrial settings by allowing flawless mobile broadband and ultrareliable low-latency communications (URLLC). The present article investigates the foundations of MIMO and MU-MIMO, how they are included into 5G new radio (NR) standards, and what part beamforming, spatial multiplexing, and channel estimation play in them. Among the subjects addressed are hardware complexity, pilot contamination, and channel state information (CSI) acquisition. Real-time inference and task scheduling in E5G-SPF are powered by machine learning for predictive analytics and reinforcement learning for dynamic resource allocation. These techniques enable adaptive decision-making and efficient task management. 5G networks use MIMO and MU-MIMO to manage the great rise in user demand and data traffic. Without these technologies, which this paper contends are necessary to open the path for future developments in the 6G network, the expected performance targets of 5G cannot be reached. © 2025 Elsevier B.V., All rights reserved.