High-growth enterprises face unique financial challenges due to rapid scaling, dynamic market demands, and fluctuating cost structures. Accurately forecasting operational expenditure (OPEX) is therefore critical for sustaining profitability, optimizing resource allocation, and maintaining liquidity. This paper proposes a conceptual forecasting model designed to enhance the accuracy and responsiveness of OPEX prediction in high-growth organizations. The model integrates financial analytics, machine learning algorithms, and business process metrics to capture non-linear expenditure patterns linked to expansion activities, digital transformation, and workforce scaling. It emphasizes the incorporation of internal performance indicators—such as productivity ratios and cost elasticity—with external variables, including inflation, market volatility, and supply chain dynamics. By conceptualizing a hybrid framework that combines econometric modeling and predictive analytics, the study aims to bridge the gap between traditional financial forecasting methods and data-driven decision systems. Furthermore, it examines how the proposed model supports scenario planning and strategic cost optimization through continuous learning and adaptive calibration. The review synthesizes theoretical perspectives, empirical findings, and managerial implications to establish a foundation for future research in predictive financial management for high-growth enterprises. The model’s conceptualization offers actionable insights for finance leaders, data scientists, and policymakers seeking to improve expenditure predictability in fast-scaling business environments.