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 biologging techniques, to estimate both energy expenditure and intake in 48 Adélie penguins (Pygoscelis adeliae) during the chick-rearing stage. We employed the machine learning algorithm random forest (RF) to predict accelerometry-derived metrics for feeding behaviour using depth data (our proxy for energy acquisition). We also built 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 predicted energy expenditure. Movement metrics derived from the RF algorithm deployed on depth data were able to accurately detect the same feeding behaviour predicted from accelerometry. The RF predicted accelerometry-estimated time spent feeding more accurately compared with historical proxies such as number of undulations or dive bottom duration. The proposed framework is accurate, reliable and simple to implement on data from biologging technology widely used on marine species. It enables coupling energy intake and expenditure, which is crucial to further assess individual trade-offs. Our work allows us to revisit historical data, to study how long-term environmental changes affect animal energetics.