The rapid growth of digital services, global connectivity, and data-intensive applications has driven enterprises to adopt cloud computing as the primary platform for application deployment and service delivery. Cloud environments provide elasticity, on-demand resource provisioning, and operational cost optimization; however, merely migrating traditional applications to the cloud does not guarantee performance improvement or scalability. Many legacy enterprise systems were designed as tightly coupled monolithic applications, which struggle to handle fluctuating workloads, distributed user bases, and continuous availability requirements. As a result, achieving scalability in cloud-enabled enterprises has become fundamentally an architectural challenge rather than an infrastructure problem. This review presents a comprehensive analysis of scalable architectural paradigms used in modern enterprise cloud systems. It examines the evolution from monolithic applications to distributed models including service-oriented architecture (SOA), microservices architecture, container-based deployment platforms, serverless computing, and event-driven architectures. For each model, the paper analyzes structural characteristics, operational principles, and suitability for different workload patterns. Particular attention is given to how these architectures enable horizontal scaling, independent deployment, fault isolation, and resource efficiency. In addition to architectural models, the review investigates practical scalability strategies such as load balancing, dynamic autoscaling, database sharding, caching mechanisms, and redundancy-based fault tolerance. The study further discusses implementation challenges that arise in distributed systems, including network latency, data consistency management, observability complexity, and expanded security attack surfaces. These challenges highlight the trade-offs between performance, reliability, and system complexity in cloud-native environments. The paper also explores emerging technological directions shaping future enterprise computing, including hybrid and multi-cloud deployment models, edge computing integration for latency-sensitive applications, and artificial intelligence–driven predictive autoscaling. By synthesizing current architectural approaches and operational practices, this review provides a structured conceptual foundation for understanding scalable system design. The study is intended to assist students, early researchers, and practitioners in selecting appropriate architectural strategies for building resilient, high-performance, and cost-efficient cloud-enabled enterprise systems.