<p>Polytetrafluoroethylene (PTFE) is a critical material for manufacturing ultra-clean fluid control components in integrated circuit fabrication equipment. Its primary processing method is mechanical machining, during which the unique thermodynamic properties of PTFE lead to significant changes in material characteristics influenced by temperature variations. Due to the unique temperature-dependent behavior of PTFE, temperature measurement during the cutting process is challenging. Therefore, a temperature prediction method is needed to accurately forecast the temperature variations in different regions of PTFE during machining. To improve the machining quality of PTFE and accurately predict the internal temperature rise under different cutting parameters, this study proposes a cutting process heat distribution prediction model specifically tailored for PTFE, which differs fundamentally from traditional metal cutting heat models in its treatment of low thermal conductivity, high elasticity, and temperature-sensitive mechanical properties unique to PTFE. The PTFE constitutive model used in the model is derived by fitting the tensile experimental data curves obtained under different temperature conditions. The Johnson–Cook model was first evaluated and found inadequate for PTFE, leading to the adoption of a polynomial constitutive model that more accurately captures the nonlinear stress–strain behavior of the experimental grade PTFE. Experimental validation using both thermocouple sensors and infrared thermometry demonstrated that the predictive model accurately aligns with the temperature increase observed during machining under various cutting parameters. A linear correction method was further applied to reduce systematic bias, achieving an R² of 0.9. The findings provide a reliable framework for selecting optimal cutting parameters that meet quality requirements.</p>