Sepsis stays a significant worldwide health challenge, necessitating early detection to improve patient outcomes. This study utilizes clinical metrics such as heart rate, oxygen saturation, respiratory rate, temperature, arterial blood oxygen saturation, lactate levels, white blood cell count, hours between ICU admit and hospital admit time and Intensive Care Unit Length of Stay (ICULOS) to evaluate sepsis risk. After analyzing all these clinical parameters, this research helps in detection of sepsis risk by giving significant insights for immediate clinical intervention. The method helps healthcare providers by giving early reliable predictions instead of only relying on results of time-intensive lab test, thus ensuring prompt, powerful patient care by bridging the time gap. The future work will be of integrating this into hospital patient monitoring setup, expanding its scope to numerous patient demographics, and exploring its application in remote health patient monitoring systems to enhance accessibility and diagnostic accuracy.