The swift development of Deep Reinforcement Learning (DRL) has facilitated notable advancements in addressing intricate sequential decision-making challenges. Nonetheless, scalability continues to pose a significant challenge when DRL is utilized in environments with high-dimensional state spaces, like autonomous driving, robotics, and financial modeling. When the dimensionality of state representations rises, conventional reinforcement learning methods encounter the curse of dimensionality, resulting in poor exploration, elevated computational expenses, and unstable convergence (Sutton & Barto, 2018). This study explores the scalability challenges of DRL in these environments and examines cutting-edge methods aimed at tackling these issues. Methods such as function approximation with deep neural networks, dimensionality reduction, representation learning, and hierarchical reinforcement learning are analyzed for their efficacy in enhancing scalability (LeCun et al., 2015). Moreover, recent developments including attention mechanisms, distributed training frameworks, and model-based reinforcement learning are examined to emphasize their contribution to improving performance in high-dimensional environments (Mnih et al., 2015; Silver et al., 2016). The research addresses the trade-offs between computational efficiency and learning precision, offering insights into enhancing DRL frameworks for practical applications. Through the integration of existing studies, this paper seeks to highlight significant limitations and suggest future research paths to facilitate scalable and effective DRL systems