This chapter presents the application of artificial neural network in the design of wideband and circularly polarized microstrip antennas based on variations of the E-shape configuration. Embedding resonant slots within air-suspended microstrip patches enhance bandwidth without increasing patch area, enabling compact single patch and high-performance antenna designs. The study focuses on regular and offset E-shape microstrip antennas, asymmetrical slot-loaded microstrip antennas, and multi-slot configurations for achieving broadband and dual band circular polarization across different frequency ranges. A low-cost FR4 substrate suspended above the ground plane is used in all the designs. Artificial neural network models are trained on extensive data sets covering substrate thicknesses from 0.02λg to 0.1λg and frequencies from 600 to 6000 MHz. Each dataset includes key antenna parameters such as patch and slot dimensions, resonance frequency, and feed point location. The trained artificial neural network models predict optimal design parameters with high accuracy, resulting in simulated resonance frequencies with errors below 2%. This approach facilitates efficient and accurate design of wideband and circularly polarized slot-loaded microstrip antennas over a broad range of operating conditions.