The growing complexity of mobile ecosystems demands frameworks that simultaneously address cross-platform compatibility (iOS, Android, and web) and advanced context-awareness. This paper presents CAMS-F, an evolution of the Context-Aware Mobile Systems (CAMS) framework, which integrates Model-Driven Development (MDD) with modern cloud services to overcome existing limitations in the development of multiplatform context-aware applications. CAMS-F introduces three key innovations: a domain-specific language (DSL) for declarative context modeling with automated Flutter/Dart code generation; native integration of essential cloud services, including Firebase Firestore for real-time data management, Firebase Cloud Messaging for notifications, and Google Maps API for high-precision geolocation; and Infrastructure-as-Code (IaC) deployment via Terraform or Pulumi for reproducible and scalable cloud provisioning. A logistics case study was conducted to validate the framework's capabilities. The results demonstrate a 35% reduction in development time compared to native approaches, real-time adaptation to IoT sensor data with latencies below two seconds, scalability to support 10,000 concurrent devices, and geolocation accuracy of up to 98% under operational conditions. These outcomes position CAMS-F as a viable and efficient solution for domains requiring robust cross-platform context processing, including smart city infrastructure, logistics optimization, and real-time monitoring systems. By combining enhanced developer productivity through MDD with high operational performance via optimized cloud integration, CAMS-F addresses critical gaps in current cross-platform development paradigms.