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Sepsis is a severe medical illness with over 1.7 million cases reported each year in the United States. Early diagnosis of sepsis is cr- tical to adress adecuate tre remains a major challenge in healthcare due to the nonspecificity of the initial symptoms and the lack of currently available biomarkers that demonstrate sufficient specificity or sensitiv- ity suitable for clinical practice. Wearable optical technologies, such as photoplethysmography (PPG), whic uses optical technology to measure changes in blood volume in peripheral tissues, enabling continuous mon- itoring. Identifying modest physiological changes that indicate sepsis can be challenging since they occur without a body reaction. Deep Learning (DL) models can help overcome the diagnostic gap in sepsis diagnosis and intervention. This study analyzes sepsis-related characteristics in PPG signals utilizing a collection of waveform records from both sepsis and control cases. The proposed model consists of five layers: input sequence, long short-term memory (LSTM), fully-connected, softmax, and classi- fication. The LSTM layer is chosen to extract and filter features from cycles of PPG signals; then, the features pass through a fully-connected layer to be classified. We tested our LSTM-based model on 915 one- second intervals to identify and classify sepsis severity. Our LSTM-based model accurately detected sepsis (91.30% for training and 89.74% for testing). The sepsis severity categorization achieved an accuracy of 85.9% in training and 81.4% in testing. Multiple training attempts were con- ducted to validate the model’s detecting capabilities. Preliminary results show that a deep learning model utilizing an LSTM layer can detect and categorize sepsis using PPG data, potentially allowing for real-time diagnosis and monitoring within a single cycle.