The Indian oil sardine occupies a vital role in India’s marine fisheries, accounting for approximately 15–20% of total marine landings. Recent years have seen a significant decline in stocks due to climate-induced oceanographic changes and human pressures, leading the fishery toward collapse. The southeastern Arabian Sea, especially Kerala, constitutes approximately 45% of India’s fish landings; however, variable trends associated with sea surface temperature (SST), salinity, precipitation, and primary productivity have resulted in erratic fishery cycles. Environmental parameters, including sea surface temperatures (27–29 °C), chlorophyll concentration, mixed layer depth, and net primary productivity, are crucial in influencing sardine abundance. Climate change impacts Net Primary Productivity (NPP), thereby modifying phytoplankton productivity, which directly influences Sardine food availability and recruitment success. Although projections suggest a 7% rise in NPP by the conclusion of the twenty-first century, this beneficial impact is mitigated by increasing SST, which diminishes fish biomass and recruitment efficacy. Extensive climate indices, such as the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO), have been shown to affect Sardine abundance more profoundly than the previously considered impacts of the El Niño-Southern Oscillation (ENSO). Moreover, overfishing and alterations in recruitment patterns have intensified stock declines, with the 2012 peak in landings succeeded by an 82% collapse within 3 years due to excessive fishing effort and adverse environmental conditions. Model projections under climate change scenarios (RCP 4.5 and RCP 6.0) suggest a transient increase in Sardine populations until 2050, succeeded by a significant decline due to elevated sea surface temperatures, irregular precipitation, and habitat degradation. The absence of thorough habitat suitability models under fluctuating oceanographic conditions constitutes a significant research deficiency. Advanced machine learning models, including Generalized Additive Models (GAM), Boosted Regression Trees (BRT), and Random Forest (RF), possess the potential to enhance predictions of Sardine abundance. The socio-economic consequences of diminishing sardine stocks are significant, influencing coastal livelihoods and food security. Impoverished fishing communities, heavily dependent on sardines for income and sustenance, endure significant economic distress due to stock depletion. This study highlights the critical necessity for adaptive fisheries management, climate-resilient conservation strategies, and interdisciplinary research to guarantee the sustainability of sardine fisheries in Indian waters.