Phylogenetic trees are central to evolutionary biology, comparative genomics, microbial ecology, and molecular systematics. As sequencing technologies continue to generate high-volume and high-diversity datasets, researchers increasingly rely on interactive visualization platforms to inspect, annotate, and communicate tree-based knowledge. Although current tools such as the Interactive Tree of Life (iTOL) provide sophisticated mechanisms for tree display and metadata integration, the interpretation of large phylogenetic trees remains largely manual, time-consuming, and dependent on expert judgment. In this paper, we propose an artificial intelligence–assisted framework for automated annotation and pattern detection in large phylogenetic trees. The framework combines tree-structural descriptors, branch-level metadata, taxonomy-aware node information, and supervised machine learning models to identify biologically meaningful clades, classify functional patterns, prioritize labels, and recommend visual annotations for interactive display environments. A Random Forest model is used as the primary predictive engine due to its interpretability and robustness, while clustering and anomaly-detection components are incorporated to reveal hidden evolutionary structures. The proposed framework is designed to complement visualization systems such as iTOL by automatically generating annotation suggestions, highlighting candidate clades, and assisting users in exploratory phylogenetic interpretation. Benchmark experiments on microbial, functional, and taxonomic tree datasets indicate that AI-assisted annotation improves consistency, speeds up interpretation, and enhances the discovery of relevant biological patterns. The study establishes a foundation for next-generation intelligent tree visualization platforms that move beyond static rendering toward semi-automated evolutionary knowledge extraction.
