Multi-target　inverse　design,　which　involves　designing　multiple　targets　with　different　optimization　objectives,　becomes　a　key　focus　in　mechanical　metamaterials　(MMs)　design.　Specifically,　many　practical　applications　impose　varying　requirements　for　different　sections.　For　instance,　the　heel　of　a　sole　demands　to　provide　support,　while　the　arch　should　be　comfortable,　adequately　supportive,　and　lightweight.　However,　existing　approaches,　such　as　topology　optimization,　typically　focus　on　optimizing　MMs　for　specific　objectives,　e.g.,　high　strength.　Worse,　these　approaches　are　often　inaccurate　and　time-consuming,　even　just　for　a　single　target.　In　this　work,　based　on　graded　triply　periodic　minimal　surface　(TPMS)　architectures,　a　machine-learning-powered　approach　is　proposed　for　rapid,　accurate,　and　multi-target　MM　inverse　design　by　employing　a　six-parallel　pipeline　network　architecture　and　utilizing　deep　networks　to　map　structural　parameters　to　mechanical　curves.　The　most　suitable　results　are　selected　based　on　the　target　curves　and　other　performance　requirements,　which　can　be　derived　from　the　structural　parameters.　The　approach　achieves　a　normalized　root-mean-square　error　(NRMSE)　of　2.49%　on　the　test　dataset　and　outputs　corresponding　design　parameters　within　seconds,　simultaneously　meeting　multiple　targets.　Finally,　such　an　approach　is　demonstrated　in　designing　soles　suitable　for　various　gait　scenarios　and　foot　deformity　treatments.