The development of galaxy images classification automated schemes is necessary to identify, classify, and study the evolution and formation of galaxies in our universe as it is one of the main challenges faced by astronomers today. Scientists can also build a deeper understanding of galaxies evolution and formation by classifying them into various classes. This paper proposed a robust novel hybrid automated intelligent algorithm based on neutrosophic techniques (NTs) and machine learning techniques for classifying the galaxy morphological astronomical images into various types of galaxies images (Hubble types) based on its features into three main classes; Elliptical, Spiral and Irregular. A nine classifiers performance was assessed based on the machine learning (ML) techniques by usinga combination of a sets of morphic features (MFs); obtained from image analysis and principal component analysis (PCA) features. The results indicated that; the classifier which called, multilayer perceptron (MLP) gives the better results for the features set consisting of nine MFs and 24 PCs features among all tested cases; Mean squared error (MSE) = 0.0021; Normalized mean squared error (NMSE)= 0.0371; Correlation coefficient (r) = 0.9889, and the Error = 0.7751 with an accuracy 99.2249 %. Then, to improve the system efficiency; the neutrosophic techniques were applied in combination with the classifier that gave the best results in the previous step on the same extracted features to get a three robust component namely; membership, indeterminacy and non-membership components to fed to the neural network. The results showed that; the combination between the NTs and MLP classifier for (MFs with 4PCs) gives the best results; MSE = 0.0001; NMSE = 0.0009; r = 0.9997, and Error = 0.4212 with an accuracy about 99.5788 % in total for all chosen sets of features. The results showed the high performance of the proposed method comparing with other methods. The experimental results are performed based on a sample from (EFIGI) catalog.