In Mobile Ad Hoc Networks (MANET), a massive investigation has been conducted in recent years for increasing the efficiency of routing protocols. MANETs are dynamic, infrastructure-less wireless networks where each mobile node functions as both host and router. Due to frequent topology changes, limited bandwidth and energy constrains, efficient and reliable routing remains a critical challenge in MANETs. Traditional routing protocols often struggle to maintain performance in such highly dynamic environments. In recent years, Artificial Intelligence (AI) and optimization techniques are emerged as promising solutions to enhance routing efficiency, adaptability and robustness. This paper presents a comprehensive review of the current state-of-the-art in AI and optimization-based routing protocols in MANETs. Various methodologies including Machine learning (ML), Deep learning (DL) and Optimization techniques are analyzed and compared in terms of performance metrics such as packet delivery ratio, delay, energy consumption and scalability. The review also identifies key challenges, limitations and future research directions to guide the development of next-generation intelligent routing solutions in MANETs.