Financial strength, as one of the key indicators of sustainability and resilience in insurance companies, reflects their ability to fulfill long-term obligations and maintain operational continuity under changing economic conditions. The aim of this study was to examine financial strength in insurance companies and identify the key factors influencing it by leveraging machine learning algorithms. To this end, financial and operational data of insurance companies over recent years were collected and processed. Initially, the data were prepared using cleaning and normalization techniques, and subsequently, financial strength prediction models were developed using several machine learning algorithms, including Random Forest, Gradient Boosting, Support Vector Machine (SVM), and Artificial Neural Networks (ANN). The models’ performance was evaluated using metrics such as accuracy, R-squared, and the area under the curve (AUC) to identify the best-performing algorithm. The results indicated that models based on Gradient Boosting and Random Forest achieved the highest prediction accuracy in classifying companies according to their financial strength levels and outperformed traditional financial analysis methods. Furthermore, variable importance analysis revealed that factors such as capital adequacy ratio, operational profitability, loss-to-premium ratio, and liquidity had the most significant impact on financial strength. This research not only provides an efficient model for predicting financial strength but also demonstrates that the use of advanced machine learning algorithms can greatly assist managers and decision-makers in the insurance industry in risk identification, financial crisis prevention, and organizational sustainability improvement. The findings of this study can serve as a foundation for developing intelligent financial strength assessment systems in the national insurance sector and pave the way for future research on the application of artificial intelligence in the financial management of insurance companies.