Parkinson’s disease (PD) is a progressive neurodegenerative disorder that significantly impacts the motor and non-motor functions of affected individuals. Early detection of PD is crucial for timely intervention and effective management, as its hallmark motor symptoms, such as tremors and rigidity, appear later in the disease’s progression. Voice analysis has emerged as a promising non-invasive tool for early diagnosis, as vocal changes often precede motor symptoms. This study explores the use of voice features, such as fundamental frequency, intensity, and timbre, for classifying Parkinsonian voices. We implemented a voice classification system based on Mel-Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), and RASTA-PLP algorithms, integrated with an Arduino microcontroller for real-time feedback. The system classifies voices as either healthy or pathological and provides immediate visual feedback through LED indicators. The classification accuracy was evaluated using the leave-one-super-trial-out (LOSO) method, with results demonstrating excellent performance, achieving up to 100% accuracy for various datasets and algorithm combinations. This approach provides a novel, accessible method for the early detection of Parkinson’s disease, facilitating early intervention and ongoing monitoring. Roving current diagnostic tools by combining efficiency, accuracy, and accessibility.