Flood susceptibility mapping (FSM) is a crucial component of flood management strategies; however, traditional statistical and machine learning methods for FSM are limited in their predictive capabilities. FSM approaches typically use static inputs, relying solely on geospatial factors, and typically overlook the spatiotemporal aspects (antecedent conditions) that trigger flood events. This study addresses this gap by developing a hybrid model that combines static geospatial features with dynamic temporal meteorological data, which is often excluded in FSM. The proposed hybrid model consists of two branches: (1) a 2D Convolutional Neural Network (CNN) to extract the features from geospatial inputs (i.e., slope and surficial geology) and (2) a Convolutional Long Short-Term Memory (ConvLSTM2D) network to learn the temporal antecedent conditions from Daymet precipitation, temperature, and snow-water equivalent. This model was trained and tested in the Saint John River basin, New Brunswick, Canada — a region that has experienced significant historical flooding. Three hyperparameters were investigated: temporal sequence length (1–4-month timesteps), resampling ratio (0.1-0.7), and positive class weight (1.5 or 2.0). The optimal model (F1 = 0.89) was achieved with a 3-month timestep, a 0.2 resampling ratio, and a 1.5 positive class weight, capturing the full snowmelt-to-rain spring cycle and outperforming models with 1-, 2-, or 4-month timesteps. The proposed 2D CNN-ConvLSTM2D architecture is effective in simultaneously learning the static geospatial features and temporal meteorological sequences, highlighting the importance of seasonal antecedent conditions in FSM.