Some limitations of SEM include that

• it's a large-sample technique (typically not suitable for small samples)
• it's definitely a more complex technique that requires deeper statistical/technical knowledge to be applied properly and meaningfully than simpler techniques such as conventional OLS regression
• due to its complexity, more can go wrong with SEM than with other techniques. For example, you can run into model convergence problems or improper solutions (e.g., solutions/models with negative variance estimates). This may be confusing and intimidating to beginners and may require expert knowledge to fix the problem.
• things can go wrong or be difficult to do right when data conditions are messy (e.g., heavily non-normal data, clustered data etc.). But this is true also for many other statistical techniques.

Overall though, I would say SEM has more advantages than disadvantages relative to other statistical approaches.

I am in total agreement with Christian Geiser .

Thank you so much Christian Geiser for your thoughtful and analytical explanation.

Structural Equation Modelling is a method used for testing and estimating causal relations using a mixture of statistical databases and qualitative assumptions in statistics. This can be used for exploratory and confirmatory modelling. In the exploratory method, its exploration of the model with the study in another method confirmatory modelling typically begins with a hypothesis that gets presented in a causal model and the model is tested with the measurement database to find how the model fits. When the Structured Equation Model (SEM) is used purely for exploration, this is generally in the reference to exploratory factor analysis (EFA) as in the psychometric proposal. According to Bagozzi (1981), the Structured Equation Model (SEM) denotes different cases like Factor analysis, path analysis and regression. The Structured Equation Model (SEM) provides the ability to construct latent not measured variables directly, here variables are projected in the model from numerous measured variables each of which is called a latent variable. This allows the modeller to note down the unreliability of measurement in the model, which in theory allows the structural relations between latent variables to be estimated (Bagozzi& Yi, 1988; Byrne,2020)