This work employs efficient and reliable machine learning algorithms to not only simulate and analyze a social network but also recommend potential friends for users based on their friends' connections within the social network and based on their professions. To achieve its objectives and ensure successful recommendations, the program utilizes object-oriented Python programming. This multifaceted approach guarantees that recommendations are tailored to individual professions and connections existing within the network. The system leverages a hybrid approach that combines two techniques: collaborative filtering and content-based filtering. This approach extends beyond traditional methods by analyzing not only user connections but also user professions.