The inner detection of magnetic flux leakage (MFL) is an important way of nondestructive testing (NDT) in pipeline. For the nondestructive evaluation, anomaly edge detection is an important part because its accuracy directly affects the subsequent inversion process. Due to the data noise, the accuracy of edge detection is greatly reduced, especially for complex anomaly. Meanwhile, general machine learning algorithms take more time to process huge MFL data. To address these problems, an edge detection algorithm based on data fusion and wavelet transform is proposed. This algorithm is based on wavelet multiscale transformation and decomposition. The data layer fusion, feature layer fusion and decision layer fusion are combined. First, the original data are transformed by multicolor space, and the transformation results are fused together. The fused data are transformed by wavelet multiscale. Secondly, wavelet multilevel decomposition is executed for each scale data, and the wavelet modulus maximum edge detection is performed for each level. In addition, the edge detection results are combined with detail decomposition coefficient for fusion and reconstruction. Finally, the edge detection results with multiple scales are fused to obtain the final detection edge. Experiments are conducted on the simulated data and the real pipeline dataset, respectively. Results are compared with other edge detection algorithms, including Sobel, Canny, Roberts, Prewitt, and Log. Experimental results demonstrate that the proposed anomaly edge detection method has better performance than traditional edge detection methods. OA evaluation for our method exceeds 70%, which satisfies requirement of practical application.