New area of image processing termed "digital image forensics" aims to gather quantifiable proof of a digital image's authenticity and place of origin. Detection of forgery images to look for copied and pasted portions; however, depending on whether the copied portion underwent post-processing before being transferred to another party, the detection method may differ. Zernike Moments and Scale-Invariant Feature Transform (SIFT) combined are unique techniques that aid in the identification of textured and smooth regions. But compared to SIFT separately, this combination is the slowest. So in the proposed work, Block based image division and SIFT based key point detection model is developed to detect forgery images. The gathered images are poor visual quality and various dimension, so it is resized and converter grayscale conversion. In addition, pixel values of images are improved using optimal Gaussian filter and adaptive histogram equalization which remove noise and blurring based on sigma value. Then, using the SIFT key point extraction algorithm to extract the image's key point and compute the feature vector of each key-points. In that using a block based matching technique to split the pre-images into blocks, and each blocks are diagonally subdivide. Length of the feature vector is computed using Zernike moments of each blocks. Both SIFT features and Zernike moments features are matched to identify the manipulated image from the given data. The proposed model provides 100% recall, 98.2% precision, and 99.09% F1_score. Thus provide the proposed model was effectively detects forgery image in the given data.