Temporal pattern recognition is the process of identifying and interpreting patterns in data that change over time, crucial for applications like speech recognition, financial forecasting, and climate modeling. It involves techniques that account for the sequential nature of data, enabling systems to predict future events or understand time-dependent phenomena accurately.