We develop the Multi Ensemble Machine Learning Model (MEMLM) for reconstructing palaeoenvironments from microfossil assemblages. The machine-learning approaches, which include random tree and natural language processing techniques, substantially outperform classical approaches under cross-validation, but they can fail when applied to reconstruct past environments. Statistical significance testing is found sufficient to identify these unreliable reconstructions.