Automated　model　generation　(AMG)　can　avoid　manual　trial-and-errors　in　the　artificial　neural　network　(ANN)　model　development　process　for　microwave　design.　This　paper　reviews　recent　advancements　in　using　Bayesian-based　AMG　methods　for　ANN　modeling　in　microwave　design.　The　first　method　offers　an　efficient　solution　to　determine　the　minimum　number　of　hidden　neurons　required　to　attain　maximum　accuracy　in　a　single　hidden　layer　Multi-Layer　Perceptron　(MLP).　The　second　method,　which　extends　the　first　method,　systematically　determines　the　optimum　configuration　of　an　MLP　model　with　multiple　hidden　layers,　leading　to　improved　accuracy　with　a　comparable　number　of　network　parameters　and　computational　resources.　To　showcase　the　benefits　of　the　Bayesian-based　AMG　methods,　two　microwave　filter　examples　are　utilized.