Cost estimation is one of the vital processes in construction management that needs to be done early on in any project to determine the project’s budget. The accuracy of the cost estimate is a key factor in the success of construction projects since it enables project managers to successfully control the project’s expenses. Therefore, contractors need to constantly improve the accuracy of their estimated costs to be able to achieve their targeted profit. Construction costs mainly consist of direct costs and indirect costs. Generally, indirect costs can be categorized into two types: site overheads and general overheads. In a construction project, overheads, particularly site overhead costs, make up a considerable portion of a contractor’s budget. Accordingly, accurately estimating the site overheads of construction projects is a crucial task that needs to be done to manage projects efficiently. Thus, the main objective of this research is to develop an artificial neural network model that predicts the percentage of site overheads in construction projects, with the model being applied in the Egyptian construction industry. The major factors affecting the site overheads were identified through an extensive literature review, which included project type, project location, project duration, contract type, project direct cost, client type, class of contracting company and lastly macroeconomic indicators such as inflation rate, interest rate and currency exchange rates. In addition, cost data related to the previously mentioned factors from 55 real-life projects executed and completed in the past 6 years (from 2018 to 2024) were obtained to be used as a database for the learning process of the ANN model. Cost data from 6 projects that were not included in the database were then used to test the model. The model architecture consisted of 9 input neurons, 2 hidden layers and 1 output neuron representing the percentage of site overheads. The developed model allows its users to predict the percentage of site overheads in the early stages of the projects with a high level of accuracy.