Abstract: As oilfield development enters the middle and late stages, formation energy deficit becomes increasingly severe and injection-production conflicts become more prominent. Conventional empirical injection allocation and numerical simulation methods often face challenges such as high computational cost, difficult history matching, and insufficient prediction accuracy when applied to complex reservoirs with strong heterogeneity and rapid dynamic changes. To address these problems, an injection-production optimization method integrating production dynamic data, physical constraints, and adaptive machine learning is proposed. In this method, LSTM and XGBoost are used to construct a parallel prediction framework, and the prediction model is selected through multi-index competition. Kalman filtering and nonlinear diffusion filtering are adopted to reconstruct injection-production response signals, and a physics-constrained ANN combined with gradient sensitivity analysis is used to invert inter-well connectivity coefficients. On this basis, a dual-objective injection allocation optimization model considering both oil production enhancement and water-cut control is established and solved using the differential evolution algorithm. A case study from B Oil Production Plant shows that the physics-constrained LSTM achieves an R² of 0.9521 and reduces the MSE to 0.6513, outperforming the standard LSTM and XGBoost in both prediction accuracy and physical consistency. The inter-well connectivity inversion results identify dominant high-water-cut flow channels and agree well with geological understanding. After optimization, block oil production increases from 175.00 m³/d to 176.10 m³/d, with an incremental oil production of 1.10 m³/d. The results indicate that coupling data-driven models with seepage-physics constraints can reduce reliance on repeated history matching, improve the evaluation efficiency of injection-production schemes, and provide technical support for refined injection allocation and stable oil production with water control in high-water-cut oilfields.