The Conjugate Gradient Method is an iterative algorithm for solving large systems of linear equations with a symmetric positive-definite matrix, commonly used in numerical analysis and optimization. It efficiently finds the minimum of a quadratic function without the need to compute matrix inverses, making it suitable for large-scale problems.