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Stress is a common experience with significant impacts on our health, which has sparked an increase in research aimed at detecting stress. This paper explores the effectiveness of using Electrocardiogram (ECG) signals as a key indicator for stress detection. What distinguishes our research is the carefully constructed architecture of our deep neural network model, coupled with the meticulous optimization strategies employed. We trained and evaluated our model using the comprehensive WESAD[10] dataset, which includes PANAS[10] self-reports. Our proposed model demonstrates remarkable improvements in accuracy and F1 score compared to previous attempts, highlighting the potential of deep learning techniques in understanding and predicting stress levels based on physiological data and harness its power to offer an objective measure of the body's response to stress. The results of the DeepChill demonstrate the effectiveness of the deep learning model in detecting stress from ECG signals, highlighting its potential for health monitoring and intervention. This contributes to the field of affective computing by providing a reliable and efficient method for stress detection. The proposed fully connected neural network model has attained an impressive accuracy of 92% surpassing the 85% accuracy reported on the WESAD dataset. [ABSTRACT FROM AUTHOR]