<p>The rapid advancement of electronic technologies necessitates the development of materials with tailored properties for multifunctional applications. However, there are significant challenges include a fundamental gap in connecting quantum-level behavior to macroscopic properties, data scarcity, and difficulties in integrating multidisciplinary datasets. This paper aims to analyze recent advancements and propose integrated frameworks to bridge these gaps by leveraging artificial intelligence (AI) and machine learning (ML) with a comprehensive review methodology and critical analysis of properties, types, their diverse applications coupled with AI-driven approaches, including generative models, physics-informed neural networks, and autonomous laboratories, for predicting and optimizing electronic materials. Key findings highlight their diverse applications and discovery such as perovskites, 2D mate- rials, and high-temperature superconductors—and in optimizing electronic, thermal, and magnetic characteristics. Recent studies indicate that AI-driven approaches can improve prediction accuracy and enable inverse design in selected systems. These approaches have the potential for significant impact on materials discovery and integration, potentially leading to a transformation of the electronic materials landscape. This paper underscores the future potential of AI-driven paradigms to revolutionize the electronic materials landscape by integrating computational prediction with experimental validation for multifunctional real-world applications.</p>