Systematic review of artificial intelligence approaches in antenna design and optimization for internet of things applications

Artificial intelligence (AI) and machine learning (ML) are increasingly being applied to antenna modeling, performance prediction, optimization, inverse design, and reconfiguration for Internet of Things (IoT) applications. This article presents a systematic literature review and evidence mapping to identify AI approaches used in antenna design and optimization and examine their performance, validation, and IoT-related requirements. Studies published between January 2021 and June 2025 were identified through IEEE Xplore, ScienceDirect, Scopus, SpringerLink, Wiley Online Library, and MDPI, using predefined search and eligibility criteria. A total of 35 reports, comprising 23 primary AI antenna studies and 12 review or survey articles, were included and classified by AI approach, antenna design task, application domain, validation level, and IoT requirements. The literature shows that supervised learning, deep learning, evolutionary and metaheuristic optimization, surrogate modeling, and hybrid approaches are used across antenna development. AI supports performance prediction, design space exploration, parameter optimization, and reduction of selected EM evaluations within specific workflows. However, reported accuracy, computational efficiency, and antenna performance improvements vary with datasets, antenna configurations, validation procedures, and computational methods, limiting direct quantitative comparison. Experimental and measured studies provide stronger evidence of practical feasibility than simulation-only or conceptual investigations, while AI-assisted methods generally complement rather than replace physics-based EM simulation and final verification. Key challenges include dataset standardization, generalization to unseen configurations, inconsistent reporting, resource constraints, and limited experimental evidence for adaptive AI-controlled antenna systems. Future research should emphasize reproducible datasets, leakage-resistant evaluation, uncertainty-aware modeling, quantitative AI–EM comparisons, resource-efficient implementation, and experimental validation.