Abstract Energy governs species’ life histories and pace of living, requiring individuals to make trade-offs. However, measuring energetic parameters in the wild is challenging, often resulting in data collected from heterogeneous sources. This complicates comprehensive analysis and hampers transferability within and across case studies. We present a novel framework, combining information obtained from eco-physiology and bio-logging techniques, to estimate both energy expended and acquired on 48 Adélie penguins ( Pygoscelis adeliae ) during the chick-rearing stage. We employ the machine learning algorithm random forest (RF) to predict accelerometry-estimated foraging behaviour using depth data (our proxy for energy acquisition). We also build a time-activity model calibrated with doubly labelled water data to estimate energy expenditure. Using depth-derived time spent diving and amount of vertical movement in the sub-surface phase, we accurately predict energy expenditure (R² = 0.70). Movement metrics derived from depth data modelled with the RF algorithm were able to accurately (accuracy = 0.82) detect the same foraging behaviour predicted from accelerometry. The RF more accurately predicted accelerometry-estimated time spent foraging (R² = 0.81) compared to historical proxies like number of undulations (R² = 0.51) or dive bottom duration (R² = 0.31). The proposed framework is accurate, reliable and simple to implement, enabling to couple energy intake and expenditure, which is crucial to further assess individual trade-offs. We provide universal guidelines for predicting these parameters based on widely used bio-logging technology in marine species. Our work allows us to revisit historical data, to study how long-term environmental changes affect animals’ energetics. Summary statement Using machine learning, we estimated energy expenditure and foraging activity of free-ranging Adélie penguins using depth data recorded with bio-logging devices.