Insulator production is a quality-critical manufacturing domain in which small variations in raw-material composition, forming pressure, drying conditions, kiln temperature, and surface finishing can produce latent defects that later affect mechanical strength, dielectric performance, and field reliability. Conventional inspection methods often rely on end-of-line rejection and manual visual checks, which are reactive, labor-intensive, and weak at identifying process drift early enough to prevent scrap. This paper proposes a manufacturing and quality analytics framework for insulator production that integrates statistical process control, machine learning-based defect prediction, and reliability analysis in a single decision-support pipeline. A structured synthetic dataset is used to emulate a porcelain insulator plant, containing process, material, and inspection variables such as SiO₂, Al₂O₃, Fe₂O₃, moisture content, press force, firing temperature, glaze thickness, dimensional deviation, and defect outcome. The methodology combines data preprocessing, feature engineering, process capability analysis, XGBoost classification, and Weibull-based reliability estimation. In the simulated evaluation, the proposed hybrid framework outperforms rule-based quality screening and conventional classifiers, achieving an accuracy of 96.1%, precision of 95.4%, recall of 96.8%, and F1-score of 96.1% for defect prediction. The results indicate that embedding predictive analytics into the manufacturing workflow can reduce non-conforming output, improve process stability, and support risk-aware maintenance and replacement planning. The paper concludes that a unified analytics architecture is more effective than isolated inspection methods for high-volume insulator production and offers a practical foundation for Industry 4.0 deployment in ceramic and electrical insulator plants.