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Due to their high power-conversion efficiency and low fabrication cost, Perovskite solar cells (PSCs) have been introduced as a promising technology in photovoltaics. However, optimizing their functional performance remains challenging due to the complex interplay among processing conditions, material structure, and resultant properties. This paper proposes a new conceptual framework that uses a multi-modal transformer architecture to correlate processing parameters with functional outcomes in PSCs, grounded in the processing-structure-performance (PSP) paradigm. By integrating different data modalities—such as textual descriptions of processing recipes, graphical representations of microstructures, and numerical performance metrics—the framework provides a unified theoretical model for understanding and predicting PSP relationships. Recent advances in artificial intelligence have enabled the architecture to use transformer-based mechanisms for cross-modal attention and fusion, facilitating the extraction of latent correlations without empirical data. This approach addresses limitations in traditional modeling by providing a scalable, interpretable means to conceptualize how variations in processing influence structural evolution and, ultimately, device efficiency and stability. The innovation of this framework lies in its modality-agnostic design, which theorizes emergent patterns in high-dimensional PSP spaces. Potential implications include accelerated theoretical insights for materials design, fostering advancements in sustainable energy technologies. This purely conceptual work synthesizes literature to establish a foundation for future theoretical explorations in applied artificial intelligence for materials science.