The purpose of this study is to develop and validate a risk-driven predictive model for estimating project duration and cost in irrigation canal lining projects, where uncertainties often lead to delays and budget overruns. Ninety-three factors were first reduced to twenty using AHP–RII (Cronbach’s ). A multi-layer perceptron (128–64–32, ReLU, Adam, early stopping) was trained on 5000 simulated scenarios and validated on eight projects with leave-one-project-out cross-validation. The model had (training), 0.82 (testing), and made errors within the limits of 0.87 months (time) and EGP 102,500 (cost) on average.The developed model was deployed as a Python-based desktop application, enabling engineers and planners to generate accurate time and cost forecasts during early project stages. This research introduces an integrated ANN-based framework that combines expert-driven risk assessment with machine learning, providing a practical decision-support tool for infrastructure projects.