The current USEPA cancer risk assessment for dichloromethane (DCM) is based on deterministic physiologically based pharmacokinetic (PBPK) modeling involving comparative metabolism of DCM by the GST pathway in the lung and liver of humans and mice. Recent advances in PBPK modeling include probabilistic methods and, in particular, Bayesian inference to quantitatively address variability and uncertainty separately. Although Bayesian analysis of human PBPK models has been published, no such efforts have been reported specifically addressing the mouse, apart from results included in the OSHA final rule on DCM. Certain aspects of the OSHA model, however, are not consistent with current approaches or with the USEPA’s current DCM cancer risk assessment. Therefore, Bayesian analysis of the mouse PBPK model and dose–response modeling was undertaken to support development of an improved cancer risk assessment for DCM. A hierarchical population model was developed and prior parameter distributions were selected to reflect parameter values that were considered the most appropriate and best available. Bayesian modeling was conducted using MCSim, a publicly available software program for Markov Chain Monte Carlo analysis. Mean posterior values from the calibrated model were used to develop internal dose metrics, i.e., mg DCM metabolized by the GST pathway/L tissue/day in the lung and liver using exposure concentrations and results from the NTP mouse bioassay, consistent with the approach used by the USEPA for its current DCM cancer risk assessment. Internal dose metrics were 3- to 4-fold higher than those that support the current USEPA IRIS assessment. A decrease of similar magnitude was also noted in dose–response modeling results. These results show that the Bayesian PBPK model in the mouse provides an improved basis for a cancer risk assessment of DCM.