This thesis explores the role of DNA methylation outliers in breast cancer within the framework of the epigenetic field defect hypothesis. The hypothesis posits that stochastic epigenetic alterations in histologically normal tissue may signal early carcinogenic processes. Using the GSE69914 dataset from the Gene Expression Omnibus, we analyzed methylation profiles across three tissue types: normal, cancer-adjacent normal, and cancerous breast tissue. A comprehensive preprocessing pipeline was implemented. Raw beta values were converted to Mvalues to reduce heteroscedasticity, followed by normalization, dimensionality reduction, and group labeling. Additional steps included variance-based filtering, Z-score transformations, and exclusion of low-quality or invariant CpG sites. Unlike earlier studies that rely solely on differential analysis, this work employed unsupervised machine learning algorithms for outlier detection, with the goal of identifying CpGs whose methylation values deviate substantially from the typical population-level distribution.