Dark patterns are manipulative designs that hide deception behind visually appealing User Interface (UI) elements. They are incorporated into the UI, with the primary aim of gaining profit by manipulating the users psychologically and financially. Over the years, a substantial body of research has investigated the prevalence and impact of dark patterns across various digital platforms, including mobile applications, websites, and online games. These patterns manifest in various forms, including text, images, videos, and hyperlinks. This paper presents a Systematic Literature Review (SLR) of the most relevant and recent research on dark patterns. The review categorizes the literature across four key dimensions: types of dark patterns, UI features, detection techniques, and their associated limitations. Detection methods are further classified based on the algorithms employed, with BERT emerging as the most frequently used baseline model in these studies. Among the commonly identified dark patterns are Forced Action, Misdirection, Sneaking, Scarcity, and Obstruction. Findings suggest that Mathur's dataset, comprising information from 11K e-commerce websites, is the most widely utilized in the reviewed literature.  Along with technological aspects, we examine government regulations and guidelines aimed at eliminating dark patterns. Therefore, our review enhances understanding and supports the development of dark pattern detection by helping emerging researchers create new and effective detection algorithms.