Continuous Integration (CI) servers are widely-used and essential for automated software testing and deployment nowadays. To serve customer’s services smoothly and reliably, CI servers are expected to be fault-tolerant, leading to a critical need of their reliability and thus availability. On the other hand, they are complicated with many metrics that need to be monitored in real time from many different perspectives. Addressing this issue, several existing works have taken into account early fault detection in CI servers. However, it remains unsolved due to their proposed incomplete solutions and the challenges from the complex, dynamic nature of server metrics, prompting the development of our work. In this paper, we propose a comprehensive solution to early fault detection in CI servers. Our solution is based on machine learning and statistical methods for metric value prediction and then fault detection. For the first part, we define a new hybrid model by integrating AutoRegressive Integrated Moving Average (ARIMA)’s linear modeling with Random Forest’s capability to capture non-linear interactions. The novelty of our model is reflected by the stacking mechanism which effectively enhances its model components and further makes the model yield better prediction results than the others. For the latter, a combined statistical method is defined to accurately identify future faults associated with the server metrics. As a result, our solution is more effective while conserving computational resources for CI servers as evaluated on the real-world datasets including the metric values from CI server nodes across multiple global sites.