Hyperthermia is a form of thermal therapy used in combination with radiotherapy (RT) or chemotherapy (CTx) to enhance their effects. By using electromagnetic (EM) waves, tissue can be locally heated to temperatures between 39−44 ◦C. When applied to tumor tissue, this heating induces various biological responses that enhance both RT and CTx. The clinical workflow includes a hyperthermia treatment planning (HTP) stage, where treatment parameters are optimized to determine the phase and amplitude settings for the applicator antennas. HTP is subject to significant uncertainties. Variability in patient anatomy, dielectric and thermal tissue properties, patient positioning, and applicator modeling causes variations in the temperature distribution and thus affects the treatment. This study uses polynomial chaos expansion (PCE) to model these uncertainties and evaluate their impact on treatment parameters. PCE serves as a meta-model of the simulation software within a defined range of input variables. This meta-model allows for rapid calculation of temperature distributions, enabling the sampling of numerous scenarios to achieve statistical accuracy regarding the impact of treatment uncertainties. We successfully created a pipeline including patient modeling, treatment planning and uncertainty analysis using PCE. We developed separate smaller sub-models for different sources of uncertainty, including positioning, dielectric conductivity (σ), dielectric permittivity (ϵ), thermal conductivity (k), and perfusion rate (ω). Using these models we assessed the feasibility of building a PCE model for each uncertainty and determine the optimal settings for a comprehensive model combining input variables from different uncertainty sources. A high impact on the temperature achieved in 90% of the target volume (T90) was observed, with standard deviations observed up to 1.36 ◦C. Uncertainties in positioning, σ and ω had the largest impact, the impact of ϵ and k was significantly lower. The final model incorporated 31 parameters selected based on their impact on treatment parameters in a univariate analysis. This model was used to evaluate the combined effect of all treatment uncertainties, we did not succeed in maintaining the same accuracy as we show for the sub-models. We show that uncertainties are not necessarily additive, and that the combined effect of these uncertainties induced larger deviations in T90. The results demonstrated the potential of PCE to effectively handle complex uncertainty analysis in HTP, providing a robust framework for analyzing HTP. Future work should focus on validating these findings with a larger patient cohort to enhance the generalizability and reliability of the approach.