The digital economy’s rapid evolution resulted in the era of digital marketplaces, which are global platforms that enable transactions between consumers and sellers. These are online marketplaces that enable global transactions between buyers and sellers. In order to understand the intricate dynamics present in digital marketplaces, this research conducts an economic analysis of them using models and statistical methods. The study intends to shed light on the economic implications, efficiency, and roles of network effects and trust mechanisms in these digital platforms. It does this by utilizing a comprehensive dataset that includes transaction data, user behaviors, and marketplace features from key platforms. Regression analysis, machine learning, and network analysis are all included in the methodology, which aims to identify the causal links and predictive dynamics that shape digital marketplaces. The important roles that trust and network effects play are highlighted by preliminary data, along with the complex consequences that platform policies have on consumer welfare and competition. Despite the advantages there are drawbacks as well, including vendor lock-in, security issues, and dependence on internet connectivity. This work expands our knowledge of digital marketplaces by offering insights to platform designers, legislators, and other stakeholders in the digital economy. Future research projects and new data considerations are suggested in order to conduct a more thorough analysis of this crucial component of the digital commerce ecosystem.