Parkinson disease (PD) is a neurodegenerative disease that can impair speech production. In PD, speech impairments are typically characterised by reduced vocal loudness, monotone speech, and distorted articulation. We hypothesise that speech impairment in PD could alter energy decay patterns in speech, leading to changes in reverberation time for 60 dB of decay in sound pressure (RT60). To determine whether the reverberation characteristics can help to differentiate between healthy and PD-affected speech, RT60 was derived from speech segments extracted from recordings in Castilian Spanish and English using the Schroeder’s Integration algorithm. A t-test analysis revealed significant differences between the PD group and healthy controls in both languages. In addition, classification tasks were performed based on the descriptive statistics and temporal features of RT60 for each participant, using logistic regression, decision tree and random forest classifiers. The random forest classifier provided the best accuracy (0.77) on the Castilian Spanish data and was tested on the English data, yielding an accuracy of 0.67. This study demonstrates that RT60 can be effectively repurposed for PD detection.