The liver tumor is one of the most foremost critical causes of death in the world. Nowadays, Medical Imaging (MI) is one of the prominent Computer Vision fields (CV), which helps physicians and radiologists to detect and diagnose liver tumors at an early stage. Radiologists and physicians use manual or semiautomated systems to read hundreds of images, such as Computed Tomography (CT) for the diagnosis. Therefore, there is a need for a fully-automated method to diagnose and detect the tumor early using the most popular and widely used imaging modality, CT images. The proposed work focuses on the Machine Learning (ML) methods: Random Forest (RF), J48, Logistic Model Tree (LMT), and Random Tree (RT) with multiple automated Region of Interest (ROI) for multiclass liver tumor classification. The dataset comprises four tumor classes: hemangioma, cyst, hepatocellular carcinoma, and metastasis. Converted the images into gray-scale, and the contrast of images was improved by applying histogram equalization. The noise was reduced using the Gabor filter, and image quality was improved by applying an image sharpening algorithm. Furthermore, 55 features were acquired for each ROI of different pixel dimensions using texture, binary, histogram and rotational, scalability, and translational (RST) techniques. The correlation-based feature selection (CFS) technique was deployed to obtain 20 optimized features from these 55 features for classification. The results showed that RF and RT performed better than J48 and LMT, with an accuracy of 97.48% and 97.08%, respectively. The proposed novel framework will help radiologists and physicians better diagnose liver tumors.