Control of temperature and relative humidity in storage areas and exhibitions is crucial for long-term preservation of cultural heritage objects. This paper explores the possibilities for developing a proactive system, based on a machine-learning model (XGBoost), for predicting the occurrence of unwanted indoor environmental conditions: either a too high or a too low relative humidity, within the forthcoming 24 hours. The features used in the model was hourly indoor and outdoor climate recordings, and it was applied to two indoor heritage environments; a storage and a church building. The test accuracy (f1-score) of the model was good (0.92 for high RH; 0.93 for low RH) when applied to the storage building, but only 0.78; 0.62 (high RH; low RH) for the church building test. Challenges encountered include difficulties in obtaining good historical climate data sets for training and testing the model, and the dependency of external IT systems, which, if they fail, inactivates the model without a warning. Several issues call for more research: A desirable improvement of the model would be predictions for periods longer than 24 hours ahead, still maintaining a high test accuracy. Further perspectives of using machine learning for indoor environmental forecasting could be for indoor air pollution, or energy consumption due to climate control. This requires, however, more data to be collected, in order to get the basis for building valid machine-learning models of sufficient test accuracy.