This volume extends the general theory of Smarandache structures from its mathematical foundations to the domain of computational sciences. Building on the central principle that an ambient structure may contain proper subsystems satisfying stronger or weaker collections of axioms, constraints, or formal properties, the study develops systematic applications in computer science, artificial intelligence, information theory, cryptography, and game theory. Across these domains, computational and informational systems are treated as intrinsically heterogeneous structures in which different components may possess distinct levels of expressiveness, correctness, consistency, security, information, rationality, or strategic capability. The classical strong, weak, and strong–weak Smarandache structures are extended to (n)-structures, hierarchical and branching configurations, networked structures, multidimensional strength profiles, and dynamic systems whose structural relations may evolve over time. Particular attention is given to the formal specification of structural strength, compatibility of signatures, partial ordering of heterogeneous subsystems, and the distinction between axiomatic strength and practical superiority. The volume also investigates computational methods for identifying Smarandache structures, including graph-based representations, algorithmic detection, machine learning, neuro-symbolic approaches, and automated analysis of structural hierarchies. Through theoretical formulations, canonical examples, theorem candidates, and interdisciplinary applications, the volume demonstrates that local strengthening and weakening recur across computational, informational, cryptographic, and strategic systems. The resulting framework advances Smarandache structures as a general methodology for studying structural heterogeneity and provides a foundation for their further extension to complex, adaptive, social, and natural systems.