Recommender systems are crucial in managing the overwhelming amount of information on the Internet, helping users navigate and discover content. They are essential across various industries, including e-commerce and content streaming services, where they significantly enhance user experience by providing personalized item predictions and suggestions. Recommender systems utilize a variety of information sources to deliver these recommendations, benefiting both service providers and users. Historically, recommender systems have been recognized as social content, e-commerce product recommenders, or playlist generators for video and music services. Since their inception in the mid-1990s, these systems have evolved significantly, employing various techniques and software implementations. They are adept at predicting user preferences, making them indispensable tools in today's digital landscape. Experts and researchers continually develop new methods to improve recommender systems' efficiency, fairness, and transparency in practical applications. These systems are expected to become even more integral to our daily lives as technology advances. This research study aims to provide a systematic overview of current developments in recommender systems, covering various recommendation techniques, evaluation standards, and challenges in the field.