Abstract: The future of 6G wireless communication will transcend the boundaries of traditional communication, advancing toward multimodal functionality integration. Among these advancements, the integration of sensing, communication, and power transfer (ISCPT) introduces significant challenges in multi-objective optimization. Achieving optimal system performance under multidimensional constraints has become a core research focus. This study addresses the problem of optimizing user transmission rates under multiple constraints, including quality of service (QoS), the Cramér–Rao bound (CRB) for sensing accuracy, energy harvesting (EH) thresholds, and maximum transmission power. By leveraging beamforming techniques, this paper proposes an alternating optimization algorithm based on semidefinite relaxation (SDR). The proposed approach transforms the original non-convex problem into a semi-definite programming (SDP) model that can be efficiently solved while strictly satisfying all constraints. Simulation results demonstrate that the method significantly improves system transmission rates in typical scenarios and ensures compliance with base station transmission power, sensing accuracy, and energy supply requirements. This work provides robust theoretical support for efficient resource allocation in future ISCPT-enabled networks.