In　the　field　of　intelligent　prosthetics　(IPs),　the　establishment　of　a　natural　interaction　between　prosthetic　hands　and　amputees　holds　significant　importance　in　restoring　hand　functionality,　enhancing　quality　of　life,　and　facilitating　daily　activities　and　social　engagement.　Prior　investigations　on　surface　electromyographic　(sEMG)　signals-controlled　IPs　have　predominantly　concentrated　on　gesture　recognition,　frequently　neglecting　the　equally　significant　dimension　of　force　level.　This　study　proposes　a　control　strategy　integrating　a　multi-task　learning　(MTL)　model　to　achieve　synchronized　recognition　of　gestures　and　force　levels.　The　MTL　model,　incorporating　shared　convolutional　blocks,　self-attention,　and　multi-head　attention　layers,　enhances　prosthetic　hand　control　for　seamless　user-device　interaction.　This　study　consistently　showcases　exceptional　proficiency　in　recognizing　gestures　and　force　levels　by　conducting　meticulous　experimentation　and　validating　the　findings　using　datasets　from　diverse　participants.　Comparative　assessments　endorse　the　superiority　of　the　MTL　approach,　particularly　in　real-time　testing　scenarios.　The　findings　highlight　the　potential　of　this　innovative　myoelectric　control　strategy,　empowering　prosthetic　users　for　prompt,　precise,　and　intuitive　responses,　significantly　augmenting　their　autonomy　and　quality　of　life.　IEEE