Sepsis remains a life-threatening critical challenge in healthcare due to its rapid progression and high mortality rates. Early detection and intervention are crucial for improving patient outcomes. This paper presents a comprehensive analysis of the current landscape of early sepsis prediction using Machine Learning (ML) and Deep Learning (DL) techniques. We examine the existing literature to highlight the advancements, challenges, and future prospects of utilizing data-driven algorithms for timely sepsis prediction. Through a systematic review of research studies, we identify key trends, methodologies, and performance metrics, aiming to provide a holistic understanding of the state-of-the-art in this critical field.