Microstrip antennas are widely valued for their low profile, lightweight structure, ease of fabrication, and adaptability to mounting surfaces. However, their inherently narrow bandwidth and the demand for compact designs in modern mobile communication systems pose significant design challenges. While techniques such as slot cutting, shorting posts, and the use of high-permittivity substrates enable antenna miniaturization, they often lead to reduced bandwidth. Conversely, bandwidth enhancement strategies like stacked or gap-coupled multi-resonator designs increase antenna size and complexity. A promising alternative is the incorporation of resonant slots within the patch, which preserves the low-profile form while bringing higher order modes closer to the fundamental mode, thus enhancing bandwidth. For applications in wireless and personal communication, antennas with compact size, broad bandwidth, and circular or elliptical polarization are particularly advantageous, mitigating signal degradation from multi-path effects. This chapter presents Artificial Neural Network models for a range of compact slot cut microstrip antenna configurations including rectangular microstrip antennas, square microstrip antennas, and semi-circular microstrip antennas targeted at achieving wideband and circularly polarized responses. Each antenna’s operation is first analysed in terms of its resonant and orthogonal modes, followed by detailed artificial neural network-based design modelling.