Systematic Review of Predictive Intelligence Models in ProductCentric 6G Network Planning and Optimization

Abstract

The advent of sixth-generation (6G) mobile networks promises to revolutionize telecommunications through ultra-low latency, massive connectivity, and intelligent automation. As the industry shifts toward product-centric service delivery—focusing on usercentric applications, dynamic network slicing, and on-demand resource allocation—the role of predictive intelligence in network planning and optimization becomes increasingly vital. This systematic review synthesizes current research on predictive intelligence models applied to product-centric 6G network environments, aiming to evaluate their effectiveness, methodological diversity, and potential for real-time decision-making in highly complex systems. The review is conducted following PRISMA guidelines, encompassing studies from 2018 to 2024 across major digital libraries. Selected models include machine learning algorithms, deep learning networks, Bayesian inference systems, and hybrid approaches used for traffic forecasting, user behavior prediction, resource provisioning, and failure mitigation. The findings reveal a growing reliance on supervised and unsupervised learning, particularly in network traffic analysis and proactive quality-of-service (QoS) assurance. Furthermore, the review identifies key predictive features utilized in these models, such as historical traffic data, environmental conditions, device mobility, and service consumption patterns. Emphasis is placed on models that support intent-based networking and enable autonomous network reconfiguration. Evaluation metrics, including prediction accuracy, scalability, and computational overhead, are compared to highlight performance trade-offs in real-world deployments. The review also uncovers significant challenges, such as data sparsity, high model complexity, and the need for explainable AI to improve trust and transparency in automated decisions. Gaps in current literature point to limited research on real-time multidomain orchestration and predictive analytics tailored for vertical-specific applications like healthcare, autonomous mobility, and immersive media. In conclusion, predictive intelligence models serve as the backbone of next-generation 6G network planning and optimization. Their integration into product-centric network management paradigms offers promising directions for delivering intelligent, resilient, and user-aware telecom services. The paper concludes with recommendations for future research, including the development of lightweight models, federated learning frameworks, and integration with digital twin systems for predictive simulations.