Accurate construction cost forecasting is essential for effective project decision-making and cost control throughout the project lifecycle. Traditional cost estimation methods

which are largely based on historical data analysis

parametric models

and expert judgment

often struggle to cope with the complexity and uncertainty of contemporary construction projects. With the increasing adoption of digital technologies such as Building Information Modeling (BIM)

project management information systems

and digital procurement platforms

large volumes of cost-related data are continuously generated

creating new opportunities for data-driven cost forecasting.This paper explores the application of big data analytics and machine learning techniques in construction cost forecasting and proposes an integrated data-driven framework that links data preparation

model development

and managerial decision-making. Rather than focusing on a single forecasting algorithm

the study emphasizes the importance of aligning data characteristics

modeling approaches

and practical cost management contexts. Representative machine learning models

including neural networks

support vector machines

and tree-based methods

are discussed and compared in terms of their predictive capability

data requirements

and interpretability.