Automated detection of cancerous cells in medical imaging holds significant promise for enhancing diagnostic accuracy and improving patient outcomes. This study presents a deep learning model developed for this purpose, evaluated on a dataset of 10,000 annotated medical images. Our model achieved an overall accuracy of 95.2%, precision of 93.8%, recall of 96.5%, F1-score of 95.1%, and an AUC-ROC of 0.982. These results demonstrate superior performance compared to existing state-of-the-art models, highlighting our model's ability to accurately identify cancerous cells while minimizing false positives and false negatives. The model's architecture, a convolutional neural network (CNN), effectively captures the complex patterns indicative of cancerous cells. Techniques such as data augmentation and transfer learning further enhanced the model's training process and generalization capabilities. A detailed analysis using a confusion matrix revealed minimal errors, underscoring the model's robustness and reliability. Despite the promising results, limitations include the need for more diverse datasets and realtime implementation capabilities. Future work should focus on expanding the dataset, optimizing the model for faster inference times, and extensive clinical validation. Enhancing the model's explainability and interpretability will also be crucial for clinical acceptance. In conclusion, our deep learning model significantly advances automated cancer cell detection in medical imaging, offering high accuracy and reliability. These findings support the potential of deep learning to improve diagnostic processes and patient care in clinical settings.