Multiple factors, regarding mechanical properties, load levels, and environmental conditions, affect the fatigue lifetime of reinforced concrete (RC) bridge decks. For the mechanical behavior, previous research predicts the fatigue lifetime of RC decks as a function of their punching shear capacity, where they give less attention to other modes of failure due to experimental limitations of fatigue loading and the restriction of girders spacing in the past design practice. Nowadays, multi-scale simulation can deal with fatigue loading problems, which secures examining complex situations that cannot be easily reproduced in the laboratories of fatigue tests. In this study, various RC decks with wide range of dimensions, material properties, reinforcement ratio, and load levels are analyzed by the validated multi-scale simulation. Then, artificial neural network (ANN) based model is also proposed based on wide-range of studied cases, which estimates the fatigue life of newly constructed RC decks, where it can be the basis of performance-based design. After that, the impact of deck’s properties on fatigue life is evaluated based on the built ANN model, which matches the conceptual design of RC decks. Finally, coupling of an empirical equation and ANN model is proposed, which may support conceptual decision-making.