Multimodal and Agentic Intelligence–Driven ML Fusion for Sustainable 6G Network Slicing

Sixth-generation (6G) networks are projected to include massive machine-type communication (mMTC), ultra-reliable low-latency communications (URLLC), and enhanced mobile broadband (eMBB) to offer immersive, diverse, and latency-critical services. These heterogeneous service requirements demand intelligent and autonomous slicing mechanisms that remain reliable under dynamic traffic, multimodal context variations, and cross-layer uncertainties. Existing machine learning (ML) approaches achieve strong predictive performance but lack built-in Service-Level Agreement (SLA) validation, while rule-based strategies ensure constraint adherence yet struggle with rapidly varying 6G conditions. To address these limitations, this article proposes an Agentic Intelligence–assisted ML Fusion Framework that combines ensemble prediction with autonomous agentic reasoning.For example, an agentic controller can independently override the choice and reallocate resources to avoid service failure if an ML model predicts a slice appropriate for URLLC traffic but the expected latency exceeds the SLA level. A stacked ensemble of Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and Logistic Regression (LR) captures nonlinear and multimodal dependencies in 6G beamforming data, whereas an agentic overlay enforces throughput, latency, and energy constraints in real time. Evaluation on a public 6G IoT beamforming dataset demonstrates near-perfect accuracy (99.9%) with consistent SLA compliance, improved interpretability, and enhanced stability under fluctuating service demands. The hybrid design provides a scalable foundation for multimodal, semantic-aware, and multi-agent orchestration in next-generation 6G slicing ecosystems.