Fog-smog risk prediction platform on AWS ingests motorway sensor data, weather APIs, and historical datasets through a multi-AZ VPC with public-facing CloudFront CDN and WAF protection. Data flows through private subnets: application tier runs EC2 and ECS Fargate backends behind ALB; ML tier executes SageMaker models (Random Forest, XGBoost, SVM, ANN) and Lambda classifiers; data tier persists trained weights in S3, predictions in DynamoDB, sessions in ElastiCache, and analytics in Redshift. Step Functions orchestrates the ETL pipeline via Glue, SQS queues alerts, and RDS Aurora with multi-AZ failover ensures availability for motorway authorities and drivers. This architecture demonstrates high-availability, security-first design with Cognito authentication, Shield DDoS protection, and isolated subnets for compute, ML, and data workloads. Fork and customize this diagram on Diagrams.so to adapt the ML model stack, add additional data sources, or modify the alert routing logic. The standby SageMaker endpoint and Aurora replica in AZ-2 provide disaster recovery without manual intervention.