Artificial Intelligence (AI) is increasingly transforming modern communication systems by enabling intelligent decision-making, adaptive optimization, and predictive control across network layers. Conventional communication networks rely on static rule-based mechanisms for routing, modulation selection, congestion control, and quality-of-service (QoS) management. However, next-generation networks such as 5G and emerging 6G demand ultra-low latency, high reliability, massive connectivity, and energy efficiency under dynamic traffic and mobility conditions. These requirements make traditional approaches insufficient in complex and time-varying environments. This paper presents a comprehensive AI-driven framework for communication networks that integrates machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques for intelligent channel estimation, adaptive modulation and coding, traffic prediction, dynamic routing, and resource allocation. A layered methodology is proposed where real-time network data is collected, processed, and analyzed using AI models deployed at the edge and cloud. The proposed framework supports proactive network control by predicting congestion and link quality variations, enabling timely optimization decisions. Simulation-based evaluation is conducted using realistic network scenarios including varying traffic loads, mobility patterns, and interference conditions. Results demonstrate that AI-assisted communication improves throughput, reduces end-to-end delay, enhances spectral efficiency, and increases QoS satisfaction compared to baseline traditional approaches. The findings indicate that AI is a key enabler for self-optimizing and self-healing communication networks. Finally, the paper discusses implementation challenges such as model complexity, data privacy, and explainability, highlighting future directions for practical AI-native communication systems.