The Wavelet Transform is a mathematical technique used to analyze signals and data by breaking them down into different frequency components. Unlike traditional methods like the Fourier Transform, which only provides frequency information, wavelets can capture both frequency and location in time. This makes them particularly useful for analyzing non-stationary signals, such as audio or images, where characteristics change over time. Wavelet Transform uses small wave-like functions called wavelets to represent data at various scales. By applying these wavelets, we can compress data, detect features, and even remove noise. This versatility makes wavelet analysis popular in fields like image processing, signal processing, and data compression.