This research explores the application of artificial intelligence and statistical techniques within predictive analytics to drive economic development and enhance digital business models in smart cities. The primary objective is to assess the improving decision-making processes that support urban growth and business innovation in smart environments. The study utilizes a thorough examination of existing literature to identify gaps and build upon previous research in this domain. A comprehensive framework is developed, outlining the predictive analytics models applied to economic and digital business strategies in smart cities. This framework also evaluates the suitability and impact of various AI and statistical approaches in fostering economic growth and optimizing digital infrastructure. Hypotheses are derived from a theoretical foundation that underscores the role of AI and predictive analytics in smart city development. The study concludes by summarizing the key findings, discussing the broader implications of applying AI and statistical techniques in smart city environments, and offering recommendations for future research focused on digital business and economic strategies in urban contexts.