This article aims to help doctors diagnose patients suffering from early cancer, which is essential to improve patients' chances of survival and treatment options. However, early diagnosis remains complex due to the diversity of symptoms and medical examinations, which are often difficult to interpret. A rule-based expert system is important to help doctors diagnose a patient's cancer early. This system relies on a structured medical knowledge base and an inference engine that applies clinical rules to generate diagnostic recommendations according to the case. This article presents a rule-based expert system designed for the early diagnosis of breast, lung, and colorectal cancers. To create a knowledge base, patient data was collected from oncologists. After collecting the data, a rule base was developed in collaboration with oncologists and integrated into the system's inference engine. Finally, the system was implemented and validated in hospitals and clinics. Thus, the models were validated individually by comparing the results obtained with reality over a long period of time, and the rules were tested by apparent validation and compared with the decisions made by experts. The system's evaluation test result showed a high sensitivity of 59%. The system reduced the number of false positives and helped doctors identify high-risk patients more quickly. The rule-based expert system represents a major advancement in the medical field and could significantly improve early cancer detection.