This research highlights the critical role of forecasting in the insurance industry and emphasises the premium retention ratio (PRR) as a key internal performance indicator for evaluating insurance company operations. Traditional time series models like ARIMA and Exponential Smoothing face limitations in capturing complex data patterns. To address this, the study proposes a hybrid predictive model that combines statistical time series models (ARIMA, EXP) with advanced AI techniques (ANN, SVR) to enhance PRR prediction accuracy in Egypts Fire, Marine, and Aviation insurance sectors. Using 80% of data for training (19892015) and 20% for testing (20162021), the study demonstrates that hybrid models, particularly ARIMA-ANN and EXP-ANN, outperform conventional models. The findings suggest that incorporating ANN into these models significantly improves prediction accuracy. This research offers a novel approach to forecasting in the Egyptian insurance market and provides publicly accessible datasets for further comparative studies across different countries.