<abstract><p>We consider saddle point problem and proposed an updating $ QR $ factorization technique for its solution. In this approach, instead of working with large system which may have a number of complexities such as memory consumption and storage requirements, we computed $ QR $ factorization of matrix $ A $ and then updated its upper triangular factor $ R $ by appending the matrices $ B $ and $ \left(\begin{array}{cc} B^T &amp; -C \\ \end{array} \right) $ to obtain the solution. The $ QR $ factorization updated process consisting of updating of the upper triangular factor $ R $ and avoid the involvement of orthogonal factor $ Q $ due to its expensive storage requirements. This technique is also suited as an updating strategy when $ QR $ factorization of matrix $ A $ is available and it is required that matrices of similar nature be added to its right end or at bottom position for solving the modified problems. Numerical tests are carried out to demonstrate the applications and accuracy of the proposed approach.</p></abstract>