Anticonvulsive drug (ACD) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials before a successful treatment can be found. In this thesis we apply a novel approach, using machine learning techniques to predict epilepsy treatment outcomes of commonly used ACDs. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Our work on Mecp2-deficient mice focuses on low frequency oscillations (LFO), high frequency oscillations (HFO) and their interactions through cross-frequency coupling (CFC) to reveal iEEG based biomarkers that track epileptic seizure pathology. Our findings revealed:​ variability across discharge events using iEEG recordings, progression of longer duration discharges over five developmental time points, and the increased cross-frequency coupling index ICFC of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in discharge events. These results suggest a link between long duration discharges with elevated ICFC to the epileptic seizure pathology. Using the ICFC to label post-treatment outcomes we trained Support Vector Machines (SVM) and Random Forest (RF) machine learning classifiers on time-based, normalized power and CFC comodulogram features to predict the efficacy of ACD treatments. The results indicate that the performance of the comodulogram features yielded better predictions and were further improved when combined with time-based features. Hence, machine learning techniques were able to rank ACDs by estimating likelihood scores for successful treatment outcome. Identifying the most appropriate ACD treatment a priori would reduce the burdens of drug trials and provide patient specific treatment options that could lead to substantial improvements in patient quality of life.