When specifying a clinical objective for a target volume and normal organs/tissues in IMRT planning, the user may not be sure if the defined clinical objective could be achieved by the optimizer. To this end, we propose a novel method to predict the achievability of clinical objectives upfront before invoking the optimization. A new metric called “Geometric Complexity (GC)” is used to estimate the achievability of clinical objectives. Essentially GC is the measure of the number of “unmodulated” beamlets or rays that intersect the Region-of-interest (ROI) and the target volume. We first compute the geometric complexity ratio (GCratio) between the GC of a ROI in a reference plan and the GC of the same ROI in a given plan. The GCratio of a ROI indicates the relative geometric complexity of the ROI as compared to the same ROI in the reference plan. Hence GCratio can be used to predict if a defined clinical objective associated with the ROI can be met by the optimizer for a given case. We have evaluated the proposed method on six Head and Neck cases using Pinnacle3 (version 9.10.0) Treatment Planning System (TPS). Out of total of 42 clinical objectives from six cases accounted in the study, 37 were in agreement with the prediction, which implies an agreement of about 88% between predicted and obtained results. The results indicate the feasibility of using the proposed method in head and neck cases for predicting the achievability of clinical objectives.