Abstract: The minimum miscibility pressure (MMP) is a critical parameter that determines whether a reservoir can be explored by miscible flooding. To accurately predict the MMP, the multi-layer perceptron (MLP) prediction model was optimized using an improved mayfly algorithm (IMA). The attention mechanism was used to extract the factors affecting MMP; the optimization capability of IMA was enhanced by incorporating chaotic Sobol sequences, nonlinear inertia weights, and reverse learning methods. These improvements can provide optimal weights and thresholds for the MLP, leading to the establishment of the IMA-AmMLP model for MMP prediction. The model was validated by the case study of a block in Jilin oilfield. The results demonstrate that the IMA-AmMLP model exhibit a higher degree of fitting between the predicted and actual values, with a mean absolute error (MAE) of 1.036 MPa, a mean absolute percentage error (MAPE) of 0.024, and a root mean square error (RMSE) of 0.835, and the values were all superior to those of the original model. This indicates that the IMA-AmMLP model can more accurately predict MMP, providing a valuable reference for the exploitation and management of reservoirs using CO₂ flooding in fields.