The rapid expansion of multimodal large-scale datasets encompassing structured records, text, images, genomic sequences, and sensor streams has intensified the need for advanced analytical frameworks capable of extracting meaningful insights from ultra-high-dimensional environments. Traditional machine learning models struggle under these conditions due to feature sparsity, complex interdependencies, non-linear relationships, and the computational burden associated with exhaustive interaction search. As organizations increasingly depend on data-driven intelligence, there is a pressing need for next-generation artificial intelligence methods that can efficiently identify, model, and optimize variable interactions while maintaining strong predictive performance. This study proposes an integrated perspective on emerging AI approaches designed to operate effectively in ultra-high-dimensional spaces, highlighting transformative advances in deep representation learning, scalable feature selection, and probabilistic modeling. The paper first situates these developments within the broader landscape of big-data analytics, outlining key theoretical and computational constraints that limit conventional interaction modelling. It then narrows its focus to cutting-edge solutions such as sparse neural architectures, interaction-aware transformers, hybrid symbolic–neural systems, and automated interaction discovery frameworks powered by reinforcement learning and Bayesian optimization. These advanced techniques leverage multimodal fusion strategies that unify heterogeneous data types into shared latent representations, enabling more efficient interaction search and improved generalizability across domains. The study also examines algorithmic innovations that enhance prediction efficiency, including progressive dimensionality pruning, cross-modal attention mechanisms, and scalable regularization schemes. Practical applications are discussed across healthcare, finance, cybersecurity, and scientific research, demonstrating the value of optimized interaction modelling in real-world decision systems. Finally, the paper presents open research challenges, opportunities for future innovation, and methodological pathways for building robust, interpretable, and computationally efficient AI systems tailored to ultra-high-dimensional multimodal data.