Colorectal cancer (CRC) is characterized by the uncontrollable growth of cancerous cells within the rectal mucosa. In contrast, colon polyps, precancerous growths, can develop into colon cancer, causing symptoms like rectal bleeding, abdominal pain, diarrhea, weight loss, and constipation. It is the leading cause of death worldwide, and this potentially fatal cancer severely afflicts the elderly. Furthermore, early diagnosis is crucial for effective treatment, as it is often more time-consuming and laborious for experts. This study improved the accuracy of CRC multi-class classification compared to previous research utilizing diverse datasets, such as NCT-CRC-HE-100 K (100,000 images) and CRC-VAL-HE-7 K (7,180 images). Initially, we utilized various image processing techniques on the NCT-CRC-HE-100 K dataset to improve image quality and noise-freeness, followed by multiple feature extraction and selection methods to identify prominent features from a large data hub and experimenting with different approaches to select the best classifiers for these critical features. The third ensemble model (XGB-LightGBM-RF) achieved an optimum accuracy of 99.63% with 40 prominent features using univariate feature selection methods. Moreover, the third ensemble model also achieved 99.73% accuracy from the CRC-VAL-HE-7 K dataset. After combining two datasets, the third ensemble model achieved 99.27% accuracy. In addition, we trained and tested our model with two different datasets. We used 80% data from NCT-CRC-HE-100 K and 20% data from CRC-VAL-HE-7 K, respectively, for training and testing purposes, while the third ensemble model obtained 98.43% accuracy in multi-class classification. The results show that this new framework, which was created using the third ensemble model, can help experts figure out what kinds of CRC diseases people are dealing with at the very beginning of an investigation.