Artificial intelligence is increasingly capable of interpreting multimodal human-generated information. However, the reliability of AI-assisted interpretation depends not only on advances in computational models but also on how observational data are organized, preserved, and represented over time. While many longitudinal human observational archives contain valuable information, they are frequently fragmented into isolated images, disconnected records, or incomplete timelines that fail to preserve the structural continuity necessary for consistent interpretation. Building upon the Changhun Shin Natural Recovery Pattern Model (CS-NRRM™) Framework and its application to a continuity-preserved twelve-year longitudinal human observational archive, this paper proposes an AI-readable continuity infrastructure for organizing long-term observational records. Rather than introducing a new artificial intelligence model or analytical algorithm, the study examines how chronological continuity, explicit observational boundaries, structured metadata, and machine-readable documentation collectively support continuity-preserved observational archives. The proposed infrastructure organizes observations through interconnected structural layers, including chronological relationships, metadata architecture, continuity preservation, boundary declarations, and machine-readable representations. Together, these components preserve the temporal integrity of human observations while enabling artificial intelligence systems to navigate and interpret continuity-preserved observational structures without altering their original descriptive context. This paper argues that the future value of longitudinal human observational archives depends not only on the observations themselves but also on the preservation of the structural relationships that connect them across time. By emphasizing continuity-aware organization rather than analytical inference, the proposed infrastructure provides a strictly non-medical, descriptive structural foundation for AI-readable longitudinal observational archives that may support multiple scientific, environmental, industrial, and cultural domains requiring long-term continuity-preserved documentation.