A novel fractal geometry-based microstrip patch antenna design using Artificial neural networks (ANN) is explored in this chapter. The existing and traditional deep learning approaches, like Support Vector Machine (SVM) and Genetic Algorithm (GA) in the patch antenna design process, are reviewed in this chapter, and their efficiency, accuracy, and computational effort are highlighted. The ability of ANN to effectively handle the non-linear relationships in the antenna design process and its performance metrics in comparison with traditional techniques are highlighted in the chapter. By analyzing the results obtained through the HFSS Simulator, the outcomes of this (proposed) chapter represent the importance (efficacy) of ANN in the fractal microstrip antenna optimization process. It is noticed that the proposed design showcased improvement in performance in terms of bandwidth, gain compared with the traditional online simulator approach. The proposed method establishes a foundation for advanced research in utilizing ML and DL for antenna design, Paving the way for more efficient and advanced vehicular communication.