Maintaining the quality of indoor environments in educational facilities is crucial for student comfort, health, well-being, and learning performance. Amidst the growing recognition of the impact of indoor environmental conditions on occupant comfort, health, and well-being, there has been an increasing focus on the assessment and modeling of Indoor Environmental Quality (IEQ). Despite considerable advancements, current IEQ modeling and assessment methodologies often prioritize and limit to singular comfort metrics, potentially neglect- ing the comprehensive and holistic factors associated with occupant comfort and health. Furthermore, existing indoor environment maintenance practices and building systems for educational facilities often fail to include feedback from occupants (e.g., students and fac- ulty) and exhibit limited adaptability to their needs. This calls for more inclusive and occupant-centric IEQ assessment models that cover a broader spectrum of environmental parameters and occupant needs. To address the gaps, this thesis proposes a novel Artificial Intelligence of Things (AIoT)-based IEQ assessment framework that bridges gaps by uti- lizing multimodal data fusion and deep learning-based prediction and classification models. These models are developed to utilize real-time multidimensional IEQ data, non-intrusive occupant feedback (MFCC features from audio recordings, video/thermal features extracted by Vision Transformer (ViT)), and self-reported comfort and health levels, placing a focus on occupant-centric and data-driven decision-making for intelligent educational facilities. The proposed framework was evaluated and validated at Virginia Tech Blacksburg campus, achieving a 91.9% in R2 score in predicting future IEQ conditions and 97% and 96% accuracy in comfort and health-based IEQ conditions classifications. / Master of Science