Journal of Information Technology Review P Paramasivaiah 17 2 2026 https://doi.org/10.6025/jitr/2026/17/2/80-99 https://www.dline.info/jitr/fulltext/v17n2/jitrv17n2_3.pdf This study investigates the impact of oil price shocks on global economic vulnerability during the hypothetical 2026 US-Iran conflict, specifically focusing on the closure of the Strait of Hormuz. Integrating time-series decomposition, machine learning, and diffusion-based modeling, the research analyzes crude oil price dynamics and their macroeconomic consequences. The methodology employs seasonal trend decomposition, change-point detection, and an adapted robust SEIR framework to track shock propagation across nations. Additionally, hybrid machine learning models utilizing Gradient Boosting and Random Forest algorithms forecast price spikes and volatility. Results identify significant structural breaks on March 4 and March 9, 2026, coinciding with conflict escalation and driving Brent crude to $104 per barrel. The SEIR model categorizes economies into Susceptible, Exposed, Infected, and Resilient states, revealing high vulnerability in South Asian nations like Pakistan and Bangladesh due to import dependence. Scenario analysis projects GDP contractions ranging from -1.62% in the best-case de-escalation to -28.80% for Iran under prolonged conflict. Crucially, findings indicate that early policy interventions can reduce economic damage by 30-40%. The study concludes that combining diffusion models with machine learning enhances forecasting accuracy under geopolitical uncertainty. It emphasizes that shock severity depends not only on market dynamics but also on policy responsiveness, urging coordinated strategic reserve releases and rapid diplomatic action to mitigate systemic risks in energy-dependent economies. Future research should incorporate longer historical datasets to further refine predictive capabilities