Predicting cotton crop quality in Gujarat requires the integration of diverse data sources, including high-resolution weather data from the Indian Meteorological Department (IMD), pest and disease records from the Indian Council of Agricultural Research (ICAR), and long-term socio-economic trends. A review of existing studies reveals that weather parameters, such as rainfall, temperature, and humidity, are the most significant predictors of cotton yield, with both statistical models (e.g., multiple regression) and machine learning (ML) approaches (e.g., Random Forest, Gradient Boosting) demonstrating predictive utility. Weather-derived indices, such as drought indices and vegetation indices, enhance prediction accuracy compared to raw weather data. However, integrating pest/disease impacts and state-level trends into predictive models remains underexplored. While ML techniques outperform traditional statistical methods in capturing complex, nonlinear interactions, challenges remain in aligning heterogeneous datasets with varying spatial and temporal resolutions. Additionally, most existing models prioritize yield prediction over cotton quality metrics, such as fiber strength and length, which are critical for stakeholders. Future research should focus on developing holistic frameworks that combine IMD weather data, ICAR pest/disease records, and socio-economic trends using advanced ML methods with interpretable outputs. Such models would provide robust and actionable predictions, addressing key gaps in the field and enhancing decision-making for cotton crop quality management in Gujarat. Disease detection studies also cover where using CNN models demonstrated precisions exceeding 97–99%, clearly establishing the quantitative advantage of ML methods over traditional statistical approaches.