This study presents a novel algorithm for image classification based on a quasi- Bayesian approach and the extraction of probability density functions (PDFs). First, representative PDFs are extracted from each image using its features. Next, a meas- ure is developed to evaluate the similarity between the extracted PDFs. Finally, an algorithm is established for determining prior probabilities using fuzzy clustering techniques. By combining these improvements, we develop a more efficient algo- rithm for classifying image data. An image is assigned to a specific group if it has the highest value of prior probability and a similar level to that group. We explain the proposed algorithm step-by-step with a numerical example and clearly demon- strate its convergence. When applied to multiple image datasets, the proposed algo- rithm has shown stability and efficiency, outperforming many other statistical and machine learning methods. Additionally, we have developed a Matlab procedure to apply the proposed algorithm to real image datasets. These applications demonstrate the potential of research in various fields related to the digital revolution and artifi- cial intelligence.