Race results in Formula 1 hinge on shifting factors like tire wear, changing skies, how drivers perform, yet also unexpected events during the event. Expert guesses plus past records guide current tactics – though these struggle when surprises hit mid-race. Instead of relying only on experience, this study explores using artificial intelligence to rethink decision-making. Machine learning tools combined with simulated race runs help forecast what might happen next. Predicting each lap time matters just as much as modeling how tires fade over distance. By running thousands of possible versions of a race, the method weighs different choices. Pit stop timing gets refined not by habit but through pattern recognition across data. Win chances emerge from probability trails rather than fixed assumptions. The whole setup adapts quickly if conditions shift without warning. Decisions gain support from modeled futures instead of memory alone. What seems likely changes every few moments, matching reality more closely.  Scenarios  unfold  based  on  linked cause-and-effect chains built from real patterns. Strategy becomes less about instinct, more about tested projections. Even small advantages show up clearly