The weak electromagnetic signals originating from the brain’s neuronal activities can be assessed by electroencephalography (EEG) and magnetoencephalography (MEG). Due to the continuous time-series data of EEG and low amplitude and nonstationary characteristics, it is difficult to achieve a consistent and satisfactory diagnosis outcome. It is hard to use these signals to identify and describe neuronal activation in the brain and adequate knowledge of signal processing, statistics, and numerical techniques are required. This paper introduces an innovative hybrid approach using machine learning, i.e., Modality Integration for Neuro Signals to Enhance Accuracy (MINE-Acc) which combines EEG and MEG data to increase brain activity prediction accuracy. This approach leverages the complementary strengths of both modalities to improve the accuracy and robustness of prediction. We employed the machine learning pipeline and used a Logistic Regression (LR) classifier in the research, performed a fivefold cross validation on sample dataset given by MNE-Python and by combining modalities together the findings provide a prediction accuracy of 99.8%. Traditional methods, such as functional magnetic resonance imaging (fMRI) and Positron Emission Tomography (PET), are available to use these signals to characterize normal and pathological brain activity but there remain difficulties with integration and interpretation. They have high spatial resolution but lack real-time capabilities. The study determines the improved prediction accuracy of the activity participants engage with based on combined analysis of EEG and MEG data. We used the MNE-Python, a software package to test this novel approach.