Deeply rooted in Indian culture and tracing back to Vedic times, the practice of meditation has been a significant aspect of spiritual, mental, and physical well-being. In this research, a meticulous preprocessing pipeline, including artifact removal techniques, and spectral and spatial filtering, is employed. The Morlet Wavelet was used to generate the time and frequency images in alpha and theta bands of EEG. Two frequency bands, theta (4–8 Hz) and alpha (8–12 Hz), are investigated using a Convolutional Neural Network (CNN) architecture for classification. Performance metrics including accuracy, precision, recall, and area under the curve (AUC) are used to evaluate the model’s classification ability. The result of Alpha brainwave activity shows a significant in-crease in expert meditators, aligning with the existing literature on the association between alpha band activity and meditation. Theta wave results indicate inconsistent achievement of deep meditation states due to experimental factors.