(untitled) (Al-Farooq Journal of Sciences, 2026)
The wireless communications domain is undergoing a profound architectural transformation that extends beyond conventional performance enhancement and capacity expansion toward redefining how networks are designed, operated, and managed. In this context, artificial intelligence (AI) has emerged as a foundational enabler of this transformation, shifting modern wireless systems from static, rule-based operational paradigms toward cognitive, adaptive, predictive, and self-decision-making infrastructures [1]–[5]. This paper investigates the structural role of AI in reshaping contemporary wireless communication networks, with a particular focus on the transition from AI-assisted fifth-generation (5G) systems to AI-native sixth-generation (6G) networks. The study examines key technical pillars including intelligent spectrum management, edge intelligence, AI-driven beamforming, resource orchestration and network slicing, and AI-based cybersecurity, while also highlighting the growing convergence among communications, sensing, and intelligent computing [4]–[12]. The paper further argues that 5G-Advanced represents a strategic transitional stage in which AI capabilities are increasingly incorporated into standardized and operational network functions, especially within radio access, model lifecycle management, and intelligent automation. This evolution reflects a broader shift from “AI-enhanced networks” to “networks designed around native intelligence”. The study concludes that 6G should not be viewed merely as a faster successor to 5G, but rather as a fundamental paradigm shift toward self-learning, self-evolving, and semantically aware communication infrastructures capable of supporting future applications such as digital twins, physical AI, and semantic communications.