Journal of Intelligent Computing Puttakul Puttawattanakul, Hathairat Ketmaneechairat 17 2 2026 https://doi.org/10.6025/jic/2026/17/2/76-92 https://www.dline.info/jic/fulltext/v17n2/jicv17n2_3.pdf Real-world datasets increasingly encompass heterogeneous modalities, yet conventional fusion techniques often struggle with varying data reliability, missing inputs, and dynamic cross-modal interactions. To address these limitations, this study proposes an integrated multi-model learning framework that synergistically combines transformer, tree, and sequential based architectures. Central to this approach is the AdaptiveFusion module, which dynamically estimates modality reliability, facilitates cross modal attention, and aggregates features adaptively via a learnable gating mechanism. The framework operationalizes this architecture by integrating BERT for deep textual semantics, XGBoost for structured numerical temporal prediction, and LSTM for sequential dependency modeling. Empirical validation is conducted on a curated dataset tracking the chronological evolution of conversational AI systems, comprising categorical, temporal, numerical, and textual attributes. Experimental results demonstrate that each component effectively captures its designated data dimension: BERT achieves rapid semantic convergence, XGBoost delivers robust structured classification with high F1-scores (>0.80), and LSTM successfully identifies latent temporal trajectories. When integrated, these paradigms overcome individual limitations, yielding a holistic analytical system that enhances both predictive accuracy and interpretability. Despite constraints related to dataset scale and class imbalance, the proposed framework establishes a scalable, context-aware methodology for multimodal data fusion. This work underscores the critical value of hybrid modeling strategies in extracting comprehensive insights from complex, real-world information ecosystems.