Artificial neural networks are among the most prominent AI techniques used for early cancer detection, offering the ability to analyze large and complex medical data with high speed and accuracy. These networks simulate the human brain’s information processing, enabling them to detect subtle patterns in medical images, such as X-rays, CT scans, and MRI scans, which may be difficult for the human eye to identify. Neural networks contribute to improving tumor diagnosis at early stages, increasing the chances of successful treatment and reducing complications associated with delayed detection. This technology also allows for the simultaneous analysis of data from multiple sources, including laboratory test results and electronic health records, providing a comprehensive evaluation of the patient’s condition and supporting appropriate therapeutic decisions. The effectiveness of neural networks relies on their ability to learn from large datasets, allowing them to adapt to different types of cancer and improve diagnostic accuracy over time. They can also be integrated with deep learning techniques to design personalized models for each patient, taking into account genetic, environmental, and lifestyle factors, thereby enhancing risk prediction and offering tailored treatment plans. In addition to diagnostic benefits, neural networks reduce the time and effort required for medical image analysis, increasing the efficiency of hospitals and clinics and enabling faster, more accurate patient monitoring. However, challenges remain related to data privacy and security, as well as the need to train medical staff in correctly interpreting the results of intelligent systems for optimal use. Overall, neural networks for early cancer detection represent a breakthrough in medical diagnostics, combining artificial intelligence with medical expertise to provide accurate early-stage diagnoses, enhancing healthcare quality and improving patient recovery outcomes. Al-Mustaqbal University is the first one university in Iraq.