ABSTRACT Predicting the microscopic distribution of remaining oil in chemical flooding is challenging due to the complex interplay of wettability, interfacial tension, and flow dynamics. Existing data‐driven models often require extensive training data and lack physical consistency. To address this, we propose a physical‐constrained deep learning framework for rapid and accurate prediction. Initially, a comprehensive dataset was generated using the lattice Boltzmann method, encompassing variations in wettability, interfacial tension, displacement velocity, and viscosity ratio. A regression model was derived to quantify the relationship between these parameters and the remaining oil characteristic. We then developed a hybrid loss function combining Dice loss with a physics‐based regression loss. The network structure was established based on the U‐Net structure and a normalization network. Then, the network underwent training with various loss functions and datasets. Finally, the method predicted the microscopic remaining oil under various conditions and classified the remaining oil to examine the change patterns. The prediction results were compared with and without constraints in the loss function, revealing that this method significantly reduces dataset establishment time and data volume required. Additionally, favorable findings were obtained even with displacement conditions not included in the training set. Prediction analysis under various conditions showed that a closer oil‐wet rock ratio to water‐wet rock ratio results in more network remaining oil and less cluster remaining oil. Higher displacement velocities or lower interfacial tensions correlate with a reduced proportion of network remaining oil. This study aids in swiftly analyzing the microremaining oil alterations in chemical flooding under different conditions and provides a foundation for further exploration of remaining oil postchemical flooding.