Parkinson’s disease (PD) is a neurological disease that occurs when dopamine-producing neurons are damaged or die, leading to reduced dopamine levels in the brain. Dopamine is a critical neurotransmitter involved in movement, and its deficiency causes movement impairment, balance issues, and tremors. Additionally, up to 90% of individuals with PD experience speech impairment. Therefore, this study focuses on detecting Parkinson’s disease using two complementary diagnostic tools: Archimedean spiral drawings and voice frequency analysis. Spiral drawings help assess tremor severity, while vocal deterioration serves as a strong secondary indicator of PD. To classify spiral drawings, we employed Convolutional Neural Network (CNN), Random Forest, and AdaBoost algorithms. For voice frequency analysis, we utilized Random Forest, K-Nearest Neighbors (KNNs), and a hybrid Partial Least Squares (PLS) and Support Vector Machine (SVM) model. The combined approach of image and voice analysis enhances detection accuracy, enabling early diagnosis that is non-invasive and efficient. Our results demonstrate that CNN achieved the highest accuracy of 89.84% for spiral image classification, while Random Forest and AdaBoost both yielded 76.67%. For voice frequency analysis, Random Forest achieved a notable accuracy of 94.87%, KNN reached 91.52%, and the PLS and SVM combination achieved 88.13%. Based on its superior performance, Random Forest was selected as the preferred model for voice frequency analysis. This study highlights the potential of integrating image and voice-based diagnostic techniques to improve Parkinson’s disease detection, emphasizing its reliability, efficiency, and early intervention capabilities.