Flexibility, light weight, and mechanical robustness are the key advantages of flexible photovoltaic (PV) modules, making them highly versatile for sustainable energy solutions. Unlike traditional rigid PV modules, their flexible nature makes them incredibly versatile for harnessing energy in places where doing so was once impossible. They have a wide range of applications due to their flexibility and moldability, making it possible to conform these modules to surfaces like curved rooftops and other irregular structures. In this paper, we provide a comprehensive review of all the materials used in flexible PV modules with a focus on their role in sustainability. We thoroughly discuss the active-layer materials for crystalline silicon (c-Si)-based solar cells (SC) and thin-film solar cells such as cadmium telluride (CdTe), as well as copper indium gallium diselenide (CIGS), amorphous thin-film silicon (a-Si), perovskite and organic solar cells. Various properties, such as the optical, barrier, thermal, and mechanical properties of different substrate materials, are reviewed. Transport layers and conductive electrode materials are discussed with a focus on emerging trends and contributions to sustainable PV technology. Various fabrication techniques involved in making flexible PV modules, along with advantages, disadvantages, and future trends, are highlighted in the paper. The commercialization of flexible PV is also discussed, which is a crucial milestone in advancing and adapting new technologies in the PV industry with a focus on contributing toward sustainability.

Perovskite solar cells achieve remarkable power conversion efficiencies, yet operational stability remains a major barrier to large‐scale deployment. Reliable and rapid assessment of device state of health is therefore essential. Conventional electrical diagnostics, such as illuminated current–voltage (J–V) sweeps, provide accurate performance metrics but are time‐consuming and do not resolve spatially localized degradation, motivating non‐invasive imaging‐based alternatives. Here, a deep‐learning framework is introduced to estimate PSC efficiency retention, R _PCE = PCE _t /PCE ₀ , directly from multimodal luminescence imaging acquired during device aging. Each sample combines electroluminescence (EL), open‐circuit photoluminescence (PL _oc ), and short‐circuit photoluminescence (PL _sc ) at an aged state with device‐specific reference images at t  = 0, enabling learning of degradation‐relevant spatial changes. LumPerNet, a compact convolutional neural network, is benchmarked against a spatially homogenized control in which each luminescence channel is replaced by its spatial average while retaining the same learning framework and leakage‐aware protocol. The comparison indicates that global luminescence evolution contains most of the predictive signal, while spatial information provides a secondary contribution to robustness. These results establish spatially resolved luminescence imaging as a practical route for accelerated stability testing and non‐invasive degradation monitoring in perovskite photovoltaics.

Fully blade-coated, HTL-free carbon-based perovskite solar cells processed under ambient conditions deliver 29.34% indoor PCE, with the same architecture extended to a 10.40 cm² mini module reaching 15.34%.