This chapter explores the use of artificial neural network to model and design air-suspended microstrip antennas with U-slot configurations for broadband and circularly polarized responses. The slot cut approach enhances antenna bandwidth while maintaining a low-profile design, with U-slots serving as both inductance compensators and additional resonant mode generators. Despite extensive literature on U-slot and E-shape microstrip antennas, simplified methodologies for predicting key design parameters such as slot dimensions and feed point location across wide frequency ranges and substrate thicknesses are limited. This study addresses that gap by developing artificial neural network models for various U-slot-based rectangular microstrip antennas, including standard U-slot rectangular microstrip antenna for broadband response, asymmetric U-slot square microstrip antenna for dual band and circular polarization response, and corner-truncated U-slot square microstrip antenna for enhanced circular polarization performance. All designs utilize a suspended FR4 substrate over an air gap, and the artificial neural network is trained on extensive data sets spanning 600–6000 MHz and substrate thicknesses from 0.02 to 0.1λg. Training parameters include patch dimensions, slot geometry, and feed location obtained via simulation. The Python-based artificial neural network predicts antenna dimensions with high accuracy, achieving less than 2% error in resonance frequency. This approach offers an efficient and scalable solution for the optimized design of wideband U-slot microstrip antennas.