Knowledge of landslide displacement trends is important to understand risks and establish early warning trigger thresholds so that action can be taken to protect people and critical infrastructure. However, the availability of direct continuous displacement measurements is often limited due to relatively high costs. This has driven research to establish models that quantify relationships between landslide displacements and other measured parameters such as pore water pressures, rainfall and more recently acoustic emission (AE), so that displacement can be predicted, and hence made available at a lower cost. This paper describes an investigation of established machine learning models to predict displacements using time series measurements of AE and rainfall. Data from a case study site has been used to train models using measured displacements and then test to assess prediction accuracy. The LASSO-ELM model was shown to perform best. It was able to predict displacements to a mean absolute percentage error < 2.5% up to 60 days after the end of the training period, which is better than similar reported studies. Training a LASSO-ELM model using continuous high resolution AE measurements combined with rainfall data has potential to provide predicted displacement trends once direct measurement of displacement is no-longer available. •Machine learning approaches can automatically predict landslide displacement trends based on AE and rainfall measurements.•Four ML models were trained using displacement, AE and rainfall measurements from an active landslide over several months.•The best performing model predicted displacements to a mean absolute error <2.5% up to 60 days after the training period.