This literature review explores how machine learning techniques are used to process sequential and variable-input data in structural engineering. Covering 83 studies retrieved via 37 keywords across 7 databases, the review spans key topics such as structural health monitoring, earthquake prediction, NLP applications, wind speed forecasting, and code compliance checks. The most used algorithms include LSTM, CNN, GRU, and transformers. The study emphasizes machine learning’s capacity to uncover complex patterns in structural data and provides a comprehensive starting point for new researchers in the field.