<p>Parkinson’s disease (PD) is a multidimensional neurological condition designated by dopamine-sensitive neuron decline, which impairs generator and cognitive function. To study the dynamics of Parkinson’s disease (PD), this paper presents a novel methodology that uses Intelligent Systems Neuro-Supervised Deep Learning Networks (INSDLNs). To describe the patterns of electrical activity in the brain metrics throughout various points in the central nervous system, we suggest a model based on mathematics governed by three distinct classes. To gain a deeper understanding of the fundamental processes underlying Parkinson’s disease development, we aim to identify obscure trends within neurological data by leveraging intelligent neuro-supervised learning networks. This novel approach may lead to improved diagnostic and therapeutic approaches and holds promise for improving our understanding of the dynamics of Parkinson’s disease (PD). By utilizing the features of an architecture containing multilayer recurrent layers, the suggested Intelligent Systems Neuro-Supervised Deep Learning Networks (INSDLNs) are designed. The input and target samples for INSDLNs were organizedand constructed from reference data that was formulated using the Adams method on a range of PI scenarios for modeling using a reliable numerical solver. To evaluate the impact on patterns of brain electrical activity, this method involved moving sensor positions.The differential equations are used for creating the dataset using Mathematica’s ND solve function. The dataset for INSDLNs training was generated using the Adam stochastic solver. After that, this dataset is divided into three significant states: 80% is used for training, 10% is used for validation, and 15% is used for testing. The goal of these divisions is to effectively handle the difficulties presented by the dynamical model. The datasets, randomly divided into training, testing, and validation samples, were used to apply the INSDLNs created for the study. To ensure the model’s stability and efficacy on various data sets, the procedure for segmentation was executed by optimizing a fitness function based on mean squared error. The proposed INSDLNs demonstrate accuracy, preciseness, and security through the achievement of minimal mean squared error (MSE), complete regression analysis (Rg. As), optimized error histograms (Err. Hg), auto-correlation of error (AC of Err), cross-correlation of input with error (CCIEr), and minimal absolute error (Ab. Er).When modeling the brain rhythms of Parkinson’s disease, our INSDLNs outperformed LMBPA and BRM with very low error (MSE: 5.86E-12 ± 2.1E-12), nearly zero absolute error, and strong regression accuracy (R2 ≈ 0.998).A lower mean square error (MSE) shows that the suggested approach operates effectively and that the forecasts generated by the model are more reliable. Reaching an almost zero absolute error (Ab. Er) provides more evidence for INSDLNs. These results highlight the higher accuracy and predictive power attained by applying INSDLNs and pursuing the best possible solutions.</p>