Early diagnosis of cancers is a major requirement for the patients and a complicated job for the oncologist. If it is diagnosed early, it could have made the patient more likely to live. For a few decades, fuzzy logic emerged as an emphatic technique in the identification of diseases like different types of cancers. Mostly the recognition of cancers diseases operated with inexactness, inaccuracy, and vagueness. This paper aims to design the fuzzy expert system and its implementation for the detection of prostate cancer. Specifically, Prostate-Specific Antigen (PSA), Prostate Volume (PV), Age, and Percentage Free PSA (%FPSA) are used to determine prostate cancer risk, while Prostate Cancer Risk (PCR) serves as an output parameter. Mamdani Fuzzy Inference Method is used to calculate a range of PCR. The system provides a scale of risk of prostate cancer and clears the path for the oncologist to the determination whether their patients need a biopsy or not. This system is fast as it requires minimum calculation and hence comparatively lesser time which reduces mortality, morbidity and is more reliable than other systems, economical, and can be frequently used by doctors.