Stress is a pervasive global health issue with far-reaching effects on mental and physical well-being. Accurate and objective stress detection is crucial, particularly in clinical and real-world applications. This paper introduces the NeuroStress Analysis Pipeline (NSAP), a novel approach leveraging electroencephalogram (EEG)-derived topographic (topo) images and deep learning models for stress classification. EEG data collected during meditation sessions is transformed into topo images, which are classified using a convolutional neural network (CNN) into meditative and non-meditative states. Building on this foundation, stress levels are categorized into four tiers—low, medium, high, and very high—using a CNN-long short-term memory (CNN-LSTM) framework to analyze temporal and spatial patterns in EEG signals. A key strength of NSAP lies in its applicability to scenarios where traditional stress detection methods are impractical, such as assessing stress in unresponsive individuals. To overcome limitations posed by sparse real-world datasets, synthetic data sequences were generated by systematically varying EEG signal properties, enabling a more comprehensive analysis of feature importance and decision boundaries in the CNN-LSTM model. Experimental results demonstrate the pipeline’s strong performance in differentiating stress states and meditative phases, underscoring its robustness and versatility. This study highlights the potential of combining neurophysiological data with advanced machine learning techniques to provide deeper insights into cognitive and stress states, paving the way for personalized stress monitoring and targeted intervention strategies.