The explosive growth of massive high-dimensional datasets across domains such as healthcare, finance, social networks, cybersecurity, and environmental monitoring has created new opportunities and significant challenges for predictive modelling. Traditional machine learning methods face substantial limitations when confronted with millions of features, intricate variable dependencies, and heterogeneous data modalities. These constraints hinder their ability to efficiently identify meaningful interactions and maintain stable predictive performance under real-world conditions. In response, scalable deep learning architectures with built-in automated interaction selection have emerged as a powerful paradigm for improving robustness, efficiency, and generalizability in high-dimensional analytical environments. This paper provides a comprehensive examination of next-generation deep learning frameworks designed to automatically discover, filter, and model variable interactions at scale. The analysis begins with a broad overview of high-dimensional learning challenges, highlighting computational bottlenecks, overfitting risks, and the structural complexities inherent in massive feature spaces. It then narrows its focus to advanced architectures including sparse deep neural networks, interaction-aware attention mechanisms, graph-based neural models, and hybrid multimodal fusion systems that explicitly incorporate automated interaction selection into their learning processes. These models leverage structured sparsity, cross-layer interaction encoding, and adaptive feature weighting to enhance interpretability while reducing computational overhead. Furthermore, the paper explores how distributed training, parallel computation, and cloud-optimized architectures enable scalability across large datasets and complex decision pipelines. Practical applications in domains such as fraud detection, precision medicine, industrial automation, and high-frequency financial forecasting demonstrate the critical role of interaction-aware deep learning systems in achieving superior predictive outcomes. The paper concludes by identifying emerging research opportunities, including meta-learning strategies, automated architecture search, and real-time interaction reasoning, outlining a future path toward more resilient and computationally efficient high-dimensional learning systems.