Identical twins from the same zygote develop differential characteristics despite having the exact same genomes. In the past few decades, we have realized that there is something other than the genome that plays a huge role in regulating a plethora of processes—from stem cell differentiation to complex diseases like cancer. Therefore, it is not surprising that a lot of scientific effort has gone into studying the epigenome and its impact on diseases and metabolic processes. This chapter offers a thorough examination of epigenetics, covering everything from basic ideas and methods of detection to its complex relationships with tumour development and carcinogenesis. It explains the mechanics underlying epigenetic alterations, highlighting different detection technologies that are essential to understanding the complexity of the epigenome and producing ample data for experimental as well as computational research. With an emphasis on tumour progression, a section of the chapter explains the fundamental relationship between carcinogenesis and epigenetics. As these processes’ molecular underpinnings are uncovered, the possibility of using epigenetic markers as prognostic and diagnostic markers is highlighted. The chapter also offers insights into the creation of epigenetic treatment, highlighting the cutting-edge treatment modalities that are soon to be available. However, we are still a long way from unravelling the true potential of the epigenome. Here comes the age of artificial intelligence and machine learning. The chapter presents a comprehensive review of machine learning integration into clinical epigenetics, including the concepts of supervised, semi-supervised, and unsupervised learning, highlighting their value in forecasting disease risk, treatment outcomes, and patient stratification. The chapter explores the benefits and problems presented by machine learning techniques, including model interpretability and data standardization. To sum up, this chapter deftly navigates the complex field of epigenetics, covering everything from its foundational ideas to its state-of-the-art applications in medical settings. It highlights the revolutionary potential of data-driven approaches in understanding the complexity of the epigenome by applying machine learning techniques, thus propelling the field towards more personalized and efficacious medical interventions.