With the constant, real-time surveillance of physiological data made possible by the combination of deep learning algorithms with wearable sensors provided by the Internet of Things, the early diagnosis of neurological conditions has been considerably improved. To gather electroencephalogram (EEG), electromyogram (EMG), and gait analysis information for the early identification of diseases including Parkinson's illness, epilepsy, and Alzheimer's illness, this study suggests a deep learning- based Internet of Things system that makes use of wearable biosensors. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to interpret the information collected to find minor neurological characteristics that point to early-stage illnesses. According to research findings, the proposed method outperforms traditional machine learning methods by 18%, achieving detection precision of 95.8% for Parkinson's disease and 94.2% for epileptic seizure predictions. Furthermore, edge computing provides doctors and caregivers with almost immediate response by reducing information processing delay by 35%. To protect data privacy and preserve model flexibility to patient-specific differences, a federated learning technique is used. According to clinical research, this paradigm helps enhance initial intervention tactics by 30%, which improves outcomes for patients.