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Background The morbidity, mortality, and monetary cost associated with intracerebral hemorrhage (ICH) is devastatingly high. Several scoring systems have been proposed to prognosticate outcomes following ICH, though the original ICH Score is still the most widely used. However, recent research suggests that systemic physiological factors, such as those included in the APACHE II score, may also influence outcome. Additionally, no scoring systems to date include pre-morbid functional status. Therefore, we propose a scoring system that incorporates these factors to prognosticate 3- and 12-month functional outcomes. Methods We used the Random Forest machine learning technique to identify factors from a dataset of over 200 data points per patient that were most strongly affiliated with functional outcome. We then used linear regression to create an initial model based on these factors and modified weightings to improve accuracy. Our scoring system was compared to the ICH Score for prognosticating functional outcomes. Results Two separate scoring systems (ICHOP3 and ICHOP12) were developed for 3- and 12-month functional outcomes using GCS, NIHSS, APACHE II, pre-morbid modified Rankin scale (mRS), and hematoma volume (3-month only). Patient outcomes were dichotomized into good (mRS 0–3) and poor (mRS 4–6) categories based on functional status. AUCs in the derivation cohort for predicting mRS were 0.89 (3-month) and 0.87 (12-month); both were significantly more discriminatory than the original ICH Score. Conclusion The ICHOP scores may provide more comprehensive evaluation of a patient’s long-term functional prognosis by taking into account systemic physiological factors as well as pre-morbid functional status.