Efficient port operations depend on the disruption free operation of quay cranes (QCs), which transfer containers between vessels and internal trucks. As global container through put rises, QCs face increased pressure, resulting in accelerated wear and tear. This can lead to QC downtime, which could interrupt the entire chain of port operations. Therefore, timely identification and prediction of critical errors is essential to enable timely maintenance to lower the risk of downtime. This study utilizes two years of QC monitoring data, enriched with weather conditions and terminal operational context, alongside twenty critical error events identified by the terminal operator. The goal is to predict the occurrence and timing of these critical errors through a three-stage machine learning model. The first stage predicts the type of the next critical event based on historical error patterns, warnings, and contextual data. The second stage estimates a time window in which the event will occur. The third stage refines timing predictions when more than one hour remains. The first two stages are formulated as multiclass classification problems, and the third as a regression task. All stages utilize eXtreme Gradient Boosting (XGBoost). SHapley Additive exPlanations (SHAP) are used to identify influential features. Results show that the model predicts the next critical error type with 83% accuracy and its immediacy with 71% accuracy. However, approximating the timing of events anticipated to occur beyond one hour remains challenging. These findings support proactive maintenance planning and operational adjustments, helping port operators mitigate disruptions and enhance QC reliability.