Cost overruns remain one of the most pressing challenges in large-scale infrastructure projects. Telecom fiber rollouts, transportation systems, and energy networks often experience escalating budgets that reduce profitability, delay schedules, and undermine stakeholder confidence. Traditional forecasting methods—such as expert judgment and deterministic models—tend to be reactive and rarely anticipate risks early enough for corrective action. This research introduces a predictive analytics framework that leverages historical project data to forecast potential budget overruns and provide early-warning signals for financial decision-makers.

Budgetary performance is shaped by technical, organizational, and external factors including scope changes, terrain complexity, vendor delays, and shocks such as weather or regulatory constraints. Conventional cost-control systems often fail to account for the nonlinear interactions among these variables. By applying machine learning–based predictive modeling, this study seeks to uncover hidden patterns in historical datasets and enhance the precision of overrun forecasts.

Methodologically, the research compares regression and time-series approaches with advanced algorithms such as Random Forest, Gradient Boosting, and Support Vector Machines. Anticipated results suggest that machine learning models can reduce forecasting error by 15–25% compared to traditional methods, while also providing classification metrics to better identify projects at risk of escalation.

The contributions are threefold: (1) identifying cost drivers most strongly associated with overruns; (2) demonstrating the relative performance gains of machine learning compared with traditional approaches; and (3) outlining a practical framework for embedding predictive outputs into business intelligence dashboards used in financial planning and analysis. By focusing on infrastructure finance, particularly telecom rollouts where terrain-driven costs create high uncertainty, the study emphasizes how predictive analytics can strengthen financial governance, mitigate overruns, and support more reliable decision-making in capital-intensive projects.

Cost overruns, predictive analytics, machine learning (ML), infrastructure finance, telecom, business intelligence, financial planning

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