<p>This study investigates current advancements in the use of artificial intelligence (AI) for antenna design to explore transformational artificial intelligence strategies that boost antenna performance standards. Traditional antenna design often struggles to manage complex design parameters and effectively accomplish multi-objective optimization via traditional antenna design techniques, which depend on empirical models and repeated simulations. Reinforcement learning, deep learning, machine learning, and other AI algorithms have led to data-driven tactics that greatly increase design efficiency, accuracy, and flexibility. With a focus on their functions in resolving intricate design problems, improving performance metrics, and facilitating creative ideas, this paper examines important artificial intelligence approaches, such as neural networks, evolutionary algorithms, and RL-based frameworks. Furthermore, the study addresses the primary obstacles and compromises linked to AI applications in antenna design, such as problems with data accessibility, computing demands, and model interpretability. Further, the paper offers a systematic evaluation of the advantages and disadvantages of particular algorithms and antenna configurations, providing helpful advice for matching methods to particular design specifications. Additionally, emerging trends and future approaches that demonstrate how artificial intelligence may propel improvements in adaptive, reconfigurable, and multifunctional antenna systems are addressed. This article provides insightful guidance and relevant information for scientists and engineers wishing to apply AI to develop next-generation antenna technology by addressing significant challenges and summarizing current research results.</p>