This study provides an overview of bridge maintenance and decision-making procedures in the Philippines. It suggests employing artificial neural networks (ANN) and the Analytical Hierarchy Process (AHP) to evaluate bridge conditions using highway agency data. The purpose is to improve decision-making by examining the correlations between bridge conditions and variables such as structure, usage, materials, and age. A literature review, factor identification, and artificial neural network analysis were used to select key factors. Data were collected from the Department of Public Works and Highways, evaluated via hypothesis testing and artificial neural networks, and then validated and interpreted. AHP results indicated that structural type (0.38) was the most key factor, followed by age (0.27), material (0.21), and use (0.14). The study included participants with at least 5 years of bridge maintenance experience and a bachelor’s degree in civil engineering. The Non-Linear Time Series ANN Model had a high predictive accuracy, with an R-squared of 71.38% and a Mean Square Error of 35.29%. Additional analyses supported the model’s usefulness.