The significance of understanding the flow past a bluff body (BB) lies in its relevance to ocean, structural, and environmental applications. Capturing the transient flow behaviors with fine details requires extensive computational power. To address this, the present study develops an improved method for modeling the complex flow dynamics around a BB under steady and unsteady conditions. It is a deep learning (DL)-enhanced reduced-order model (ROM) that leverages the strengths of proper orthogonal decomposition (POD) for model reduction, convolutional neural network-long short-term memory (CNN-LSTM) for feature extraction and temporal modeling, and Bayesian optimization for hyperparameter tuning. The model starts with dimensionality reduction, followed by DL optimization and forecasting, and terminates with flow field reconstruction by combining dominant POD modes and predicted amplitudes. The goal is to establish a DL-driven ROM for fast and accurate modeling of the flow evolution. Based on the comparison of millions of data samples, the predictions from the ROM and CFD are considerably consistent, with a coefficient of determination of 0.99. Furthermore, the ROM is ∼10 times faster than the CFD and exhibits a robust noise resistance capability. This study contributes a novel modeling approach for complex flows, enabling rapid decision-making and interactive visualization in various applications, e.g., digital twins and predictive maintenance.