Social media has become an indispensable part of our everyday lives in the current digital age. These digital platforms are essential for information sharing, communication, and building meaningful relationships. Information dissemination in these social networks has become a major concern for many industries, including marketing, banking, politics, research, and education. Various models have been used to illustrate the dynamics of information sharing in these kinds of networks. Still, the concept of influence is extremely important for people and companies alike. Because influence maximization has the ability to reach and effect a large audience, it has received a lot of attention, especially in the context of social networks. This complex issue is sometimes described as the "influence maximization problem," This research presents a novel method to improve influence maximization by utilizing the Biased Bat Algorithm. Maximizing influence is a crucial problem in network analysis, which is frequently done in social networks. Influence can be compared to a cascade effect, in which a small number of people's activities set off a series of events that eventually affect a big chunk of the network. Finding these "influencers" is essential for effective resource distribution and knowledge sharing. Selecting the best vertex in the graph out of all the vertices is one of the key steps in determining the highest influence. This research presents a new method to find the maximum influence in social networks based on the Biased Bat algorithm. The proposed method aims to find the maximum influence in the social network graph that has a good fitness degree. The algorithm can identify potential influencers. Our simulations across multiple networks have unequivocally showcased the superiority of this algorithm as the preeminent and scalable solution to the influence maximization problem. The experimental outcomes clearly delineate that the employment of the MFA (Maximal First Activation) approach effectively diminishes the execution time required to approximate the maximum influence