In the dynamic landscape of global enterprise networks, accurate capacity forecasting is paramount for ensuring optimal resource allocation and preventing service disruptions. This paper presents a hybrid machine learning methodology that combines Autoregressive Integrated Moving Average (ARIMA) models with additional techniques to enhance the accuracy and reliability of network capacity forecasts. By leveraging historical traffic data and incorporating external factors, we develop a predictive model that outperforms traditional methods and adapts to the evolving demands of modern networks. The effectiveness of our approach is validated through rigorous testing against established benchmarks, demonstrating significant improvements in forecasting accuracy