The　incidence　of　hypertension　has　recently　shown　a　significant　increase　in　young　people,　with　aerobic　exercise　intervention　being　recognized　as　an　effective　approach　to　decrease　blood　pressure　(BP).　However,　BP　response　to　aerobic　exercise　can　be　highly　individualized,　and　no　research　has　been　conducted　on　predicting　the　effect　of　aerobic　exercise　intervention　for　reducing　BP　in　young　hypertensive　patients.　In　this　work,　we　use　the　data　generated　from　a　cardiopulmonary　exercise　test　(CPET)　in　young　hypertensive　patients　(before　aerobic　exercise　intervention)　to　derive　information　from　multiple　cardiopulmonary　metabolic　indices.　The　data,　presented　as　time　series,　are　then　analyzed　by　a　machine　learning　method　to　predict　the　effect　of　aerobic　exercise　intervention　in　lowering　BP.　This　study　provides　several　novel　insights　for　making　personalized　aerobic　exercise　intervention　programs　for　young　adults　with　stage　I　hypertension.　©　2019　IEEE.