Forecasts of age-specific mortality rates are a critical input in multiple research and policy areas such as assessing the overall health, well-being, and human development of a population and the pricing and risk management of life insurance contracts and longevity-linked securities. Model selection and model combination are currently the two competing approaches when modelling and forecasting mortality, often using statistical learning methods. This paper empirically investigates the predictive performance of Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) architecture in jointly modelling and multivariate time series forecasting of age-specific mortality rates across the entire lifespan. We empirically investigate different hyperparameter choices in three hidden layers LSTM models and compare the model’s forecasting accuracy with that produced by classical age-period and age-period-cohort stochastic mortality models. The empirical results obtained using data for Portugal suggest that the RNN with LSTM architecture can outperform traditional benchmarking methods. The LSTM architecture generates smooth and consistent forecasts of mortality rates at all ages and across years. The predictive accuracy of the LSTM network is higher for both sexes, significantly outperforming the benchmarks in the male population, an interesting result given the added difficulties posed by the mortality hump and higher variability in male survival functions. Further investigation considering other RNN architectures, calibration procedures, and sample datasets is necessary to confirm the robustness of deep learning methods in modelling human survival.