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Introduction: Soft tissue sarcomas (STS) are a rare group of malignancies. Diagnostic biopsies require experts and are invasive with long diagnostic intervals. Artificial intelligence (AI) based STS models may be accurate detection and categorization tools; however, their diagnostic performance is questionable. Method: The PubMed, Scopus, and Web of Science databases were searched for related studies published until January 10,2024. Studies that developed or used AI-based models to diagnose STS were included. Results: Eleven studies were included in this meta-analysis. The common effects model yielded an accuracy of 0.8923 [0.8831; 0.9016], and the random-effects model yielded an accuracy of 0.8524 [0.8132; 0.8916], The Tau2 was 0.0094 [0.0055; 0.0202], and the I² statistic was 93.2% [91.1%; 94.7%], suggesting a high level of heterogeneity among the studies. The most accurate model was the Decision Tree (DT) model used in the study by Alaoui et al. (2021), with an accuracy of 0.9900 [0.9675; 1.0125], The least accurate model was the MLP model used in the study by Yang et al. (2021), with an accuracy of 0.5720 [0.5107; 0.6333], Conclusion: AI-based models show promising results for the diagnosis of STS. However, future studies should address the value of AI in the real-world setting. [ABSTRACT FROM AUTHOR]