In the evolving landscape of digital governance, tax-related financial crimes present a persistent threat to fiscal stability, particularly in emerging economies with fragmented data ecosystems and limited enforcement capabilities. Traditional audit-based approaches to revenue protection are often reactive, inefficient, and incapable of detecting complex, evolving fraud schemes. This paper proposes a cloud-based Artificial Intelligence (AI) framework designed to proactively detect and mitigate tax leakage by leveraging predictive analytics, real-time anomaly detection, and risk modeling. The proposed model integrates machine learning algorithms with tax compliance datasets, financial transaction logs, and third-party economic indicators to flag high-risk entities and patterns indicative of evasion, underreporting, and fictitious invoicing. By deploying the AI model within a secure cloud infrastructure, the system enables scalable, on-demand analytics that align with governmental data protection policies and regulatory compliance standards. Advanced encryption, access controls, and audit trails ensure integrity and confidentiality across interagency collaborations. Furthermore, the model utilizes a hybrid AI architecture combining rule-based logic and unsupervised learning to adapt to emerging fraud tactics while minimizing false positives. This multi-tiered framework allows tax authorities to transition from post-incident recovery to strategic prevention through risk scoring and early intervention. A case study involving synthetic datasets simulating VAT fraud and corporate tax evasion demonstrates the model’s efficacy in reducing investigative lag time and improving revenue recovery rates. The paper concludes by outlining a roadmap for cross-border data sharing, AI ethics governance, and capacity building necessary to scale this model in diverse tax jurisdictions. This approach not only secures revenue but also modernizes tax administration for a digital economy.