In wireless communication systems, high-performance antenna, microwave, and radio frequency design systems are essential to meet end-user requirements. As demand for these components increases, it's crucial to design optimized structures in a short amount of time with guaranteed best results. This has led to the need for a higher level of intelligence in the design process. Artificial intelligence (AI) techniques such as evolutionary algorithms (EAs), machine learning (ML), deep learning (DL), and knowledge representation have been widely used to find parameter values of antenna and microwave components, leading to optimized designs in minimum processing time and overcoming long processing times and poor results. This chapter focuses on the major AI methods in the area of antenna, microwave, and other radio frequency (RF) components, including phase shifters, intelligent reflective surfaces (RIS), waveguides, filters, stubs, etc. The chapter discusses different EAs and ML algorithms and their use in optimizing antenna and microwave designs.