The　accurate　prediction　of　failure　pressures　in　pipelines　containing　multiple　defects　is　important　for　assessing　the　integrity　and　reliability　of　corroded　pipelines.　First,　the　effect　of　the　remaining　defects　on　the　failure　pressure　of　the　pipeline　containing　triple　defects　was　investigated　by　developing　the　finite　element　(FE)　model　of　pipelines　containing　triple　defects.　Then,　to　integrate　machine　learning　method　and　finite　element　models　to　establish　a　prediction　model　for　the　interaction　coefficients　corresponding　to　combined　corrosion　defects.　Subsequently,　a　framework　for　predicting　the　burst　pressure　of　pipelines　appliable　to　the　scenario　of　group　corrosion　defects　was　proposed　by　integrating　the　interacting　coefficient　and　the　failure　pressure　of　single　defect.　Finally,　the　accuracy　of　the　model　was　demonstrated　by　the　evaluation　indicators　and　bursting　experimental　data　of　corroded　pipeline　containing　cluster　defects.　The　framework　is　expected　to　provide　a　maintenance　foundation　for　the　integrity　assessment　of　pipelines　with　clustered　corrosion　defects.