<p>Parkinson’s disease (PD) is a neurodegenerative disorder that frequently presents with vocal impairments, making speech analysis a potentially effective noninvasive method for early detection. Current techniques reliant on conventional acoustic features frequently fail to accurately represent the nuanced, non-stationary dynamics of pathological speech. This study introduces a novel methodology utilizing time-frequency (TF) analysis through Fourier synchrosqueezing transform to improve the efficacy of PD detection from speech signals. The first speech signal is processed with FSST to produce a TF representation. We used this TF representation to calculate the energy and entropy of each frequency component and used it as a feature for classification. The various machine learning classifiers are used for classification along with the genetic algorithm (GA). The efficacy of the proposed method is assessed utilizing vowels and words from the PC-GITA dataset. The proposed method attained a classification accuracy of 91% utilizing the isolated words /atleta/. The results show that the proposed FSST-based TF features energy and entropy features are effective for PD-affected and healthy individual classification. This method has potential for creating speech-based diagnostic tools for PD.</p>