Healthcare, environmental, and financial systems generate large volumes of data that require rapid processing and intelligent interpretation. Geospatial analytics (GA) provides spatial insights for risk mapping and resource allocation, but its potential is limited without optimized database architectures that ensure scalability, low latency, and data integrity. This study developed an integrated framework combining GA, database optimization, and predictive modeling for intelligent decision-support systems (DSS). Data from healthcare (EHR, patient flow), environment (compliance records), and finance (credit risk data) were collected, cleaned, geocoded, and processed. Optimized indexing, partitioning, and caching strategies were implemented. Predictive models and dashboards were developed, and stakeholder workshops validated usability and interpretability. The framework reduced query execution times by 42%, dashboard latency by 52%, and model inference runtime by 33%. Healthcare applications improved patient bed reallocation efficiency by 25%, environmental compliance coverage increased by 18%, and credit risk prediction accuracy improved by 9%. Stakeholders reported enhanced decision-making speed and clarity. Integrating GA and optimized databases enables scalable, real-time DSS that improve operational efficiency, compliance, and predictive accuracy across sectors, providing a model for future cross-domain analytics solutions.

Geospatial analytics, database optimization, decision-support systems, predictive modeling, healthcare informatics, financial risk management

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