<![CDATA[This research aims to structuredly examine the latest developments in radar technology, combining adaptive antenna design, intelligent signal processing, and the use of Synthetic Aperture Radar (SAR) and Radar Cross Section (RCS). This study aims to address the research gap on how radar hardware innovations can be fully integrated with artificial intelligence-based algorithms, a topic that remains largely unexplored in the current literature. The method used is a Systematic Literature Review (SLR), with data from leading Q1 international journals indexed in Scopus, such as IEEE Transactions on Antennas and Propagation, IEEE Transactions on Signal Processing, IET Radar, Sonar Navigation, and Remote Sensing of Environment. The analysis process includes article searches, application of entry-exit rules, retrieval of key information, topic clustering, and comparison of methods and results to identify key trends and remaining research gaps. The results of the study indicate that the main direction of modern radar research emphasizes the integration of adaptive antennas using metamaterials and phased-array systems, plus the application of machine learning and deep learning to detect and classify targets. However, significant challenges remain in optimizing the capabilities of learning models for general use across diverse environmental conditions, as well as effectively integrating hardware and intelligent algorithms. The key contribution of this research is the creation of a conceptual framework for modern radar that connects adaptive antenna components, artificial intelligence-based signal processing, and cross-disciplinary applications such as defense, autonomous vehicles, and earth monitoring. This study is expected to serve as a foundation for further research on the development of adaptive radar systems utilizing artificial intelligence and advanced materials, while also supporting strategic policy directions in the fields of defense and environmental surveillance.]]>
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