Aiming at the problem of low positioning accuracy caused by non-line-of-sight and environmental interference in indoor positioning process, an indoor fingerprint positioning algorithm based on The Multi-Verse Optimizer- Support Vector Regression algorithm has been proposed. Firstly, the ranging values are calculated through double-side two-way ranging algorithm with ultra-wideband communication technology. Then, the ranging values are utilized as the fingerprint features to construct a fingerprint database, based on fingerprint database SVR algorithm is adopted to establish the mapping relationship between the positioning coordinates and the ranging values. Finally, the MVO algorithm is proposed to optimize the parameters of cost and γ in SVR algorithm to improve the accuracy of the positioning results. Experimental results demonstrate that the Radial Basis Function is used as the kernel function in the SVR model to significantly improve positioning accuracy. The results of MVO-SVR were compared and analyzed with those of Trilateration, Random Forest, eXtreme Gradient Boosting, and SVR algorithms. In the X direction, the average absolute error is reduced by 20.12%, 54.43%, 60.66%, and 16.21%, respectively; in the Y direction, it is reduced by 79.57%, 54.18%, 59.29%, and 38.17%, respectively. The average positioning error Ep is decreased by 60.73%, 54.38%, 60.01%, and 22.84%, respectively. Moreover, the average absolute errors in both the X and Y directions for the MVO-SVR algorithm reach the centimeter level. The results confirm that the indoor fingerprinting positioning algorithm based on MVO-SVR significantly enhances positioning accuracy and demonstrates promising application potential in complex indoor environments.