Following the COVID-19 pandemic, the transmission patterns of seasonal infectious diseases have deviated from pre-pandemic norms, highlighting the need for new early detection frameworks. Traditional outbreak detection methods, such as change point detection and hockey-stick regression, rely on retrospective analyses and cannot capture real-time epidemic transitions. We aim to propose a generalized real-time early detection framework for seasonal infectious diseases using bootstrap clustering. This approach characterizes distributional patterns in epidemic dynamics and provides uncertainty-informed detection signals. Data includes multiple seasonal diseases, such as influenza-like illness (ILI), respiratory syncytial virus (RSV), hand-foot-and-mouth disease (HFMD), and norovirus, each with distinct temporal patterns. Bootstrap clustering generates multiple resampled realizations, producing a distribution over clustering outcomes and enabling stability assessment. Building on this, we develop a distribution-based clustering framework for epidemic dynamics that allows sequential real-time updates. Using these representations, we implement a stepwise early detection system generating Caution, Alert, and Severe warnings, supporting timely and uncertainty-aware epidemic monitoring. Unlike conventional retrospective methods, this framework infers regime shifts in real time while explicitly accounting for uncertainty, offering a flexible, mathematically grounded tool for early warning and preparedness across diverse seasonal infectious diseases.