Colorectal polyps are unusual tissue growths in the colon or rectum that can progress into colorectal cancer if left undetected at an early stage. Early and accurate polyp detection during colonoscopy is crucial for effective prevention and treatment. However, manual detection is challenging due to variability in polyp size, shape and texture which can lead to missed or false diagnoses. To address these challenges, we propose a deep learning-based approach to automate polyp detection, capable of identifying even the smallest polyps and aiding early cancer prevention. This research utilizes the Kvasir-SEG dataset, a publicly available collection of annotated polyp images to train and evaluate an advanced detection model. We employed YOLO (You Only Look Once), a state-of-the-art object detection framework and trained its latest version, YOLOv11 on the Kvasir-SEG dataset. The model was enhanced through advanced data preprocessing and hyperparameter tuning, making it suitable for clinical deployment. Our approach achieved excellent results, with an Intersection over Union (IoU) score of 0.9764 and an overall accuracy of 99.00%. Also achieved a balanced precision, recall and F1-score. The detection metrics showed a mean Average Precision (mAP) of 0.9937 at 0.5 IoU threshold and 0.9935 across thresholds from 0.5 to 0.95, indicating robust and reliable performance. The model was comprehensively analyzed with SAM (Segment Anything Model), YOLO-Seg and SAM2. These results demonstrate the effectiveness of our model in accurate and consistent polyp detection. The proposed method can assist clinicians by reducing missed detections and enabling early colorectal cancer diagnosis.