To achieve synchronous positioning of multi-target magnetic dipoles with different locations， moments， and buried depths， a multi-target positioning method based on adaptive fuzzy c-means （AFCM） clustering and tensor invariants is proposed. First， based on the 2D plane grid measurement of a magnetic gradient tensor system， the target distribution area is pre-identified by using the improved tilt angle with the invariants of normalized source strength and tensor contraction. Subsequently， the tensor-derivative invariant-relation positioning method is applied to calculate the initial coordinate points of the magnetic dipoles at grid nodes in the recognition area； these points form a dense point cloud around the real position space of the magnetic source. Finally， the AFCM clustering algorithm is employed to perform 3D clustering on these point clouds of initial position solutions and automatically detect the number of cluster centroids. The estimated number of cluster centroids is the number of targets， and the cluster centroids are the target position coordinates. Then， the tensor matrix and position vector can be used to calculate the magnetic dipole moment. Simulations show that in a Gaussian noise environment with a variance of 5 nT/m， the target-number estimation accuracy of 20 magnetic dipole targets is 100%， horizontal-position estimation accuracy is greater than 91.7%， and buried-depth estimation accuracy is greater than 85.6%. Measurements reveal that the coordinate deviation of the small magnets in the measuring areas of 2.1 m × 2.1 m and 1.2 m × 1.2 m is less than 0.091 m.