This article examines a variety of Machine Learning (ML) applications in wireless communication technologies, with a particular emphasis on fifth-generation (5G) and millimeter wave (mmWave) technology. This article is a compilation of three studies on machine learning in wireless communication technologies. The paper discusses the need for machine learning to be integrated into wireless communication, the different types of machine learning techniques used in wireless communication, the benefits and potential of ML in wireless communication, and ML implementation parameters in wireless communication, as well as a study on RSS-Based Usage Classification in Indoor Millimeter-Wave Wireless Networks. Due to a broad range of service needs, varied features of industrial applications, and devices themselves, the next generation of wireless communication networks is becoming more complicated. Traditional networking methods, such as reactive, centrally controlled, one-size-fits-all solutions and traditional data processing tools, have limited capacity. In terms of operation and optimization, as well as cost-effective needs of networks and network providers, these methods do not support future sophisticated wireless networks. The rapidly growing demand for wireless communication technology necessitates research and development of new technologies and optimization methods.