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Global warming and natural disasters have wreaked havoc on Earth these days. Energy conservation has been a priority under social development pressure, especially in the building sector. Energy consumption prediction serves as a vital tool for effective building energy management, guiding energy policy making and services distribution. However, despite the application of advanced technologies, achieving accurate energy consumption prediction still faces challenges due to potential influencing factors. This study aims to explore previously unidentified factors to enhance building energy consumption prediction accuracy. An extended life cycle energy boundary of buildings including embodied, operational, and mobile energies is proposed. To optimize the prediction processes, a hybrid Life Cycle Assessment (LCA) approach and a non-linear Bidirectional Long Short-Term Memory (Bi-LSTM) model are combined. An empirical study was conducted to predict the life cycle energy consumption of urban residential buildings in China by 2035. Results show: (1) life cycle energy consumption increased dramatically from 195.837 (±11.77) Mtce in 2000 to 1151.69 (±80.38) Mtce in 2022, but then achieved a slow decline to 796.998 ((±51.46)) Mtce by 2035; (2) there were four significant development stages in the residential field: rapid growth (2000–2010), sharp growth (2010–2018), slow growth (2018–2022), and stable fall (2022–2035); (3) mobile energy related to building location accounted for up to 28% of total energy consumption, and daily commuting is its largest emitter. However, calculation errors caused by diverse data sources and the fundamental model itself need to be addressed later. This study provides an original pathway of combining LCA with a non-linear model to improve energy consumption prediction and the completed primary historical data to help local governments' energy decision-making. [ABSTRACT FROM AUTHOR]