Parkinson’s disease (PD) is a neurodegenerative brain disorder causing both physical and non-physical symptoms. Detecting it early is crucial for better treatment and outcomes. To achieve this, we have come up with a new method using speech data. We use a combination of Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL). First, we extract speech features and let a CNN learn important patterns from them. Then, we bring in an RL agent to fine-tune the selection of these features. This makes our model better at identifying subtle speech changes that can indicate early stages of PD. This approach overcomes the limitations of traditional methods by adapting to individual speech variations automatically. We tested our method on a standard PD speech dataset, and it performed well, accurately distinguishing PD patients from healthy individuals. Importantly, our model also works well with new, unseen data, suggesting its potential for use in the real world. In summary, our RL-CNN model offers a unique and valuable solution for early PD detection. In addition, our innovative approach holds promise for enhancing the accessibility of Parkinson’s disease detection. By leveraging cutting-edge technology, our model not only demonstrated high accuracy in distinguishing patients and healthy individuals but also showcased adaptability to diverse speech patterns. This breakthrough could pave the way for user-friendly tools that empower individuals to assess their own risk for Parkinson’s disease, potentially revolutionizing early detection methods and improving overall patient outcomes. Our proposed methodology (RL-CNN) performed well to give an accuracy of 97.4%.