Objectives: Infection-related consultations on intensive care units (ICU) build an important cornerstone in the care for critically ill patients with (suspected) infections. The positive impact of consultations on quality of care and clinical outcome has previously been demonstrated. However, timing is essential and to date consultations are typically event-triggered and reactive. Here, we investigate a proactive approach by predicting infection-related consultations using machine learning models and routine electronic health records (EHR). Methods: We used data from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. EHR data comprised demographics, laboratory results, point-of-care tests, vital signs, line placements, and prescriptions. Consultations were performed by clinical microbiologists. The predicted target outcome (occurrence of a consultation) was modelled using random forest (RF), gradient boosting machines (RF), and long short-term memory neural networks (LSTM). Results: Overall, 7.8 % of all admission received a consultation. Time-sensitive modelling approaches and increasing numbers of patient features (parameters) performed better than static approaches in predicting infection-related consultations at the ICU. Splitting a patient admission into eight-hour intervals and using LSTM resulted in the accurate prediction of consultations up to eight hours in advance with an area under the receiver operator curve of 0.921 and an area under precision recall curve of 0.673. Conclusion: We could successfully predict of infection-related consultations on an ICU up to eight hours in advance, even without using classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care and outcome for critically ill patients with (suspected) infections.