Recent Advances in Microstrip Patch Antenna Optimization: Methods, Design Targets, and Emerging Trends

Microstrip patch antennas (MPAs) are prevalent in contemporary wireless systems, however, their performance is highly influenced by interdependent design elements, including the dimensions of the patch, the geometry of dielectric and metallic slots, feeding configuration, substrate properties, and ground‐plane structure. This systematic review synthesizes recent advances in MPA optimization research, highlighting key design targets, optimization methodologies, performance trade‐offs, and application trends. This review advances the current literature by providing a cross‐domain synthesis that jointly links MPA design targets, optimization methodologies, electromagnetic performance trade‐offs, validation practices, and application‐frequency trends within a unified systematic framework. This search was conducted in accordance with the PRISMA framework and included papers published between January 2020 and February 2026, identified using Scopus and IEEE Xplore for English‐language publications. This resulted in 4727 records being identified; after removal of duplicates, 4700 were screened; 583 full‐text articles were assessed for eligibility; and finally, 301 studies were included in the final synthesis. Simulation‐driven or parametric‐sweep approaches were the most prevalent primary optimization methods, accounting for 151 studies (50.2%), followed by machine‐learning‐assisted methods (95 studies, 31.6%) and metaheuristic or evolutionary methods (45 studies, 15.0%). Less frequently reported were deterministic methods (6 studies, 2.0%) and DOE/Taguchi‐based methods (4 studies, 1.3%). The results demonstrate that MPA optimization is an inherently multivariable problem, with trade‐offs among antenna size, impedance matching, bandwidth, gain, and radiation efficiency. Among the included studies, 23 (7.6%) addressed sub‐GHz operation, while seven (2.3%) were associated with LoRa or LoRaWAN applications, representing a relatively small and technically important application subgroup. Overall, the findings highlight the need for integrated, experimentally validated optimization frameworks that simultaneously account for geometry, feeding configuration, substrate properties, and ground‐plane effects to achieve more balanced MPA performance.