Wireless charging of implantable medical devices, such as pacemakers, requires precisely controlled electromagnetic (EM) energy delivery to ensure efficient power transfer while preventing excessive heating of surrounding tissues. Predicting temperature rise is challenging due to anatomical complexity, including variations in skin, fat, muscle, and vascular structures. To address this, we present a two-dimensional multiphysics simulation integrating electromagnetic and thermal analyses to evaluate bioheat transfer in MRI-based torso tissue layers, including skin, fat, muscle, and vascular structures. A MATLAB-based electromagnetic solver was developed to quantify electric field distributions, Specific Absorption Rate (SAR), and heat generation from different EM sources. A dipole antenna operating at 900 MHz was employed, with systematic investigation of antenna–tissue distance, tissue permittivity, and radiated power. Near-field scattering affects electric field magnitude and distribution, producing localized heating patterns. Higher radiated power, closer source proximity, and increased tissue permittivity resulted in greater temperature rises, with high-permittivity tissues exhibiting pronounced hot spots. Transient thermal analysis was performed in ANSYS APDL using nodal heat sources derived from the MATLAB-based electromagnetic analysis. Near- and far-field evaluations and mesh-sensitivity studies ensured numerical accuracy. Validation against mathematical and simulation results produced temperature errors below 8%, confirming reliability. Three tissue configurations were compared: a single-layer microvascular domain, a multilayer skin–fat–muscle model using a rule-of-mixtures approach, and an advanced anatomical model with micro- and macro-vessels. The simple single-layer model predicted the highest temperature rise (1.13 °C), approximately 23.1% higher than vascularized models, while the advanced model showed the lowest rise (0.918 °C) due to perfusion cooling. The multilayer and advanced models differed by 8.26%, highlighting the importance of detailed vascular representation. This framework provides a validated tool for designing safety-aware wireless charging systems, enabling adaptive charging schedules tailored to patient-specific anatomical variations in skin, fat, muscle, and vascular structures, thereby enhancing both safety and efficiency.