Methylation profiling has demonstrated utilities clinical diagnoses of cancer, particularly CNS tumors; however, the unavailability of classifiers for download has proven to be an obstacle for widespread adaptation by clinical laboratories. The methylation array classification diagnostic and data integration (MACDADI) software overcomes this obstacle as it is an open-source software that can be used to classify CNS tumors based on their methylation profiles. MACDADI was developed using 3979 CNS samples comprised of 167 CNS subtypes based on 10,000 probes present in three Illumina methylation microarray platforms. The MACDADI software maintains high confidence scores (83%), as shown by validation of 230 samples. MACDADI is based on The German Cancer Research Center’s CNS classifier; however, improves upon this with use of an alternative machine learning algorithm that results in faster training speed and higher prediction accuracy. MACDADI is ideally suited for use in research and clinical settings for diagnosis of CNS tumors. It is in pre-validation testing for sarcomas, and in proof-of-concept/ planning stages for expanding into other cancer types.