The field of image processing is essential to contemporary real-time applications. Using this type of image processing approach, the digitized image can be processed to produce superior results. Many approaches that mimic image processing tools focus on improving image quality, producing noise-free images, and condensing the raw image into data that is compressed to save storage space. As one of the major causes of death globally, cancer highlights the importance of early and precise detection in order to enhance patient outcomes. Because machine learning techniques make it possible to analyze enormous amounts of biological data, they have demonstrated promising in the area of cancer detection. In order to improve the precision and effectiveness of the detection process, we provide a hybrid machine learning technique in this work for the identification of cancer cells. To increase performance overall, hybrid machine learning algorithms integrate the advantages of several different separate methods. Our approach combines supervised and unsupervised learning techniques, including k-means clustering, random forests, and assistance vector machines, to develop a more accurate and resilient cancer cell identification model. This hybrid technique takes advantage of unsupervised learning’s feature extraction and data exploration skills while utilizing supervised learning’s categorization capabilities. We used a large dataset that included several cancer kinds, such as colorectal, lung, prostate, and breast cancer, to create the hybrid model. To improve the performance of the model, we performed feature selection, preprocessing, and hyperparameter optimization.