Belief　rule-base　(BRB)　expert　system　is　one　of　recognized　and　fast-growing　approaches　in　the　areas　of　complex　problems　modeling.　However,　the　conventional　BRB　has　to　suffer　from　the　combinatorial　explosion　problem　since　the　number　of　rules　in　BRB　expands　exponentially　with　the　number　of　attributes　in　complex　problems,　although　many　alternative　techniques　have　been　looked　at　with　the　purpose　of　downsizing　BRB.　Motivated　by　this　challenge,　in　this　paper,　multilayer　tree　structure　(MTS)　is　introduced　for　the　first　time　to　define　hierarchical　BRB,　also　known　as　MTS-BRB.　MTS-BRB　is　able　to　overcome　the　combinatorial　explosion　problem　of　the　con-ventional　BRB.　Thereafter,　the　additional　modeling,　inferencing,　and　learning　procedures　are　proposed　to　create　a　self-organized　MTS-BRB　expert　system.　To　demonstrate　the　development　process　and　benefits　of　the　MTS-BRB　expert　system,　case　studies　including　benchmark　classification　datasets　and　research　and　development　(R&D)　project　risk　assessment　have　been　done.　The　comparative　results　showed　that,　in　terms　of　modelling　effectiveness　and/or　prediction　accuracy,　MTS-BRB　expert　system　surpasses　various　existing,　as　well　as　traditional　fuzzy　system-related　and　machine　learning-related　methodologies.