<p>This work presents an Adaptive Multimodal Engagement Framework (AMEF) for metaverse ecosystems supported by 6G-enabled IoT. AMEF comprises four coordinated modules: a Multimodal Data Fusion Engine (MDFE) with hierarchical attention for context-aware weighting of heterogeneous sensor streams; a Predictive Engagement Module (PEM) that applies a contextual predictive algorithm to combine temporal embeddings with user-specific factors for real-time engagement forecasting; a 6G-Optimized Communication Layer (6G-OCL) implementing a dynamic bandwidth allocation protocol to reduce latency under variable load; and a Behavioral Adaptation Engine (BAE) that translates predicted states into environment modifications. A hybrid simulation evaluated AMEF across nine scenarios (varying bandwidth, user/device density, packet loss, and interaction rates) for 600 minutes. AMEF achieved 95.4% engagement prediction accuracy, an average bandwidth utilization of 92%, mean interaction latency near 12 ms, sustained throughput around 94%, and average user satisfaction of 90.9%, exceeding five baseline methods by more than 9% on key engagement metrics. These results indicate that (i) hierarchical attention provides robust fusion under noisy or incomplete inputs, (ii) contextual temporal modeling improves next-state prediction for proactive adaptation, and (iii) latency-aware allocation in 6G-OCL maintains performance at scale. AMEF offers a deployable architecture for real-time, user-centric adaptation in 6G–IoT metaverse applications.</p>