Decompressive craniectomy is a neurosurgical procedure in which part of the skull is removed to allow a swelling brain room to expand. It is a life-saving procedure used in the treatment of medically refractory intracranial hypertension, most commonly in the setting of trauma or cerebral infarction. Essentially, any increase in the volume of the contents of the skull is first treated to be contained. If such treatments fail, then the related part of the skull is removed through surgery and the increase of volume is allowed to happen until the pathology subsides and the brain returns to its normal relaxed state. After which the defect is repaired by a procedure called Cranioplasty in which case the left-out bone is placed back and joined onto the skull. This procedure is extremely risky but is becoming very popular due to clinical successes. The determination of risk of complications at the stage of bone-replacement can be a very useful information for the doctor as well as the patient since it can determine the post-surgical treatment, care, and patient-management procedures. In this paper, we have proposed a very systematic algorithmic approach to determine this using Fuzzy Inference System. The system inputs many qualitative as well as numerical inputs and determines the risk as one of the clsses of complications; Severe, Minor, Least/None.