Parkinson's disease is a progressive neurological disorder that severely affects motor functions, making early detection critical for effective intervention and treatment. This paper presents a method for early detection of Parkinson's through gait analysis, utilizing an Internet of Things (IoT) system integrated with deep learning techniques. Gait data is collected and transmitted to a cloud database, where deep learning algorithms are applied to analyse the data and predict early signs of Parkinson's disease. The proposed system offers a non-invasive, cost-effective solution for early detection, potentially improving patient outcomes through timely diagnosis and management.