Accurately predicting earned value indicators is critical in managing bridge construction projects, as it allows for effective tracking of cost, schedule, and overall project performance. This study aims to develop and validate artificial neural network (ANN) models to predict key performance indicators (KPIs) such as the Schedule Variance (SV), Cost Variance (CV), and To-Complete Performance Index (TCPI) in bridge projects. The study utilized historical data from thirty bridge projects in Iraq, applying a structured methodology that involved the selection of relevant independent and dependent variables, data division, and ANN model development. ANN models were developed using the Neuframe program, focusing on optimizing network architecture, learning rates, and momentum terms. Data was divided into training, testing, and validation sets, with 60% allocated for training and 20% each for testing and validation. The models demonstrated high accuracy, with the SV model achieving a correlation coefficient of 88.53% and an accuracy rate of 89.91%. Similarly, the CV and TCPI models achieved 80.37% and 81.094% correlation coefficients, respectively. The study concludes that ANN models are practical tools for predicting project performance indicators. They offer robust and accurate predictions that can significantly enhance project management practices in the construction industry. These models have the potential to reduce deviations in time and cost, ultimately improving the overall efficiency and success of bridge projects and instilling hope for the future of the construction industry.