In today’s fast-paced world, the physical and mental wellbeing of the peoples are negatively affected by stressful situations. Neuromorphic data and natural language processing (NLP) techniques are integrated in this paper to detect and analyze stress. A multifaceted methodology is proposed, incorporating two key components: first, an objective measure of stress levels derived from neuromorphic data, and second, NLP algorithms to identify the causes of stress. The purpose of this study is to collect neuromorphic data that reflects physiological responses to stress using sensing technologies. Various metrics are associated along these signals, it may consist of skin sensing data, heart rate variability, and muscle tension. By analyzing these data streams, we can identify and quantify the stress levels of individuals accurately. During the study, participants asked to join the questionnaire session to fill forms to understand subjective perceptions and experiences related to stress. An NLP technique is used to analyze questionnaire responses. To understand stress triggers and patterns, neuromorphic data correlated with NLP findings. As a result, the neuromorphic data relevantly concluded with the accurate stress level predictions. This research contributes to stress analysis and detection methodologies by utilizing the synergy between neuromorphic data and NLP-based questionnaire analysis. A personalized stress management intervention tailored to individual needs could be developed based on the insights gained from this study.