This chapter presents the application of artificial neural network models for the design of slot cut circular microstrip antennas aimed at achieving wideband performance. Unlike rectangular microstrip antennas, the field distribution in circular microstrip antennas is governed by Bessel functions, with the fundamental mode being TM11. Although circular microstrip antennas inherently suppress harmonic resonances, their curved geometry limits direct gap coupling for bandwidth enhancement. Slot cut techniques offer an effective alternative, enabling wider bandwidth while maintaining compact antenna size. The chapter introduces artificial neural network models trained on simulation data over a broad frequency range (600–6000 MHz) and substrate thicknesses (0.02–0.1λg), using parameters such as patch radius, slot dimensions, and feed point location. A low-cost FR4 substrate with a suspended configuration is used throughout. The artificial neural network, implemented in Python, accurately predicts the optimal antenna dimensions for desired operating frequencies. Experimental validation confirms the effectiveness of the predicted designs, demonstrating broadband behaviour with minimal error. This approach provides a practical and efficient solution for designing advanced circular microstrip antennas using data-driven techniques.