Mobile Ad Hoc Networks (MANETs) are dynamic, decentralized networks that require efficient routing mechanisms to ensure reliable communication. Traditional routing protocols struggle with issues such as high node mobility, energy constraints, and unpredictable topology changes. This research explores the integration of artificial intelligence, specifically neural networks, to enhance routing efficiency in MANETs. Our approach leverages deep learning models to predict optimal routes by analyzing network parameters such as node density, mobility patterns, and link stability. The proposed AI-driven routing mechanism dynamically adapts to network variations, improving packet delivery ratio, reducing latency, and optimizing energy consumption. Comparative evaluations against conventional routing protocols, such as AODV and DSR, demonstrate significant improvements in network performance. The results highlight the potential of neural networks in revolutionizing adaptive routing for MANETs, paving the way for more intelligent and resilient communication systems.