Compressive sensing is a methodological approach in signal processing and data acquisition that investigates the theory and practice of reconstructing signals and data from significantly fewer measurements than conventionally required by the Nyquist-Shannon sampling theorem. This approach relies fundamentally on the signal being sparse or compressible in some known basis and employs specialized linear measurement matrices and non-linear reconstruction algorithms, typically based on convex optimization or greedy methods, to recover the original signal. Its significance lies in enabling efficient data acquisition and processing in diverse fields by reducing measurement burden, storage requirements, and acquisition time for high-dimensional data.