Macrofungi signify an extensive reservoir of bioactive metabolites. Still, their chemical description is stalled by fragmented analytical pipelines and low reproducibility. This study introduces SysFungiNet, a unified, FAIR-compliant systems-bioinformatics framework designed to accelerate bioactives discovery in non-model fungi. Unlike existing pipelines, SysFungiNet integrates LC-MS/MS metabolomics, transcriptomics, and genomic data with explainable artificial intelligence and molecular docking validation. By reconstructing biosynthetic pathways for Ganoderma lucidum and Craterellus cornucopioides, the framework achieved a Pathway Completeness Index of 0.86. An ensemble machine-learning model predicted bioactivity with an F1-score of 0.91, identifying 312 annotated metabolites. Crucially, decision-making was transparent, with SHAP analysis identifying specific chemical substructures driving predicted immunomodulation. The framework prioritized five high-confidence candidates, including a putative novel terpenoid, Cornucopiolide, which showed high binding affinity (−9.4 kcal/mol) to human immune receptors in silico. Benchmarking against MetaFungi and PhytoOmics demonstrated superior annotation accuracy and reproducibility. SysFungiNet offers a scalable, open-source ecosystem for transforming fungal bioprospecting into an evidence-driven process.