Flooding remains among the world’s most damaging natural hazards. It affects large populations and triggers major economic losses. Long-term records and recent events illustrate the scale of the problem: between 1994 and 2013, floods affected nearly 2.5 billion people worldwide. Despite advances in forecasting and disaster management, recent events (e.g., the August–September 2024 floods) left 5 million people affected and over 70 dead in Bangladesh. This massive death toll highlighted "Big Gaps" in the current flood handling system. This paper presents FloodVista, an open and reproducible computational framework designed to make flood-pattern analysis accessible to everyone without requiring extensive data or computing resources. The pipeline uses geographical elevation data and the rational method to predict flood patterns and generate a 3D model for user accessibility. It combines three key elements. First, we apply flow direction, flow accumulation, and watershed delineation algorithms to extract river tributaries and networks from a digital elevation model (DEM). Second, we use a first-order hydrological estimator based on the rational method (Q = C.i.A) with runoff coefficients inferred from automated land cover weighting and rainfall intensity obtained from historical statistics or user input. Lastly, we synthesize morphology and runoff estimates to produce relief plots, stream-order analyses, and examine the morphology of flood plains in 3D space, highlighting likely flow paths and accumulation zones under user-specified scenarios. Validation emphasizes structural and statistical plausibility rather than event-level depth predictions. Extracted networks are evaluated with Horton–Strahler stream ordering to check geomorphological realism. Modeled peak runoff statistics are compared with standard probability models and historical records to show consistency with observed hydrological variability. We modeled and tested our system on different river systems to obtain data and increase the applicability of FloodVista to flood-affected communities around the world.