In this study, we investigate the concept of $I^{*}$-statistical convergence for sequences of fuzzy numbers. We establish several theorems that provide a comprehensive understanding of this notion, including the uniqueness of limits, the relationship between $I^{*}$-statistical convergence and classical convergence, and the algebraic properties of $I^{*}$-statistically convergent sequences. We also introduce the concept of $I^{*}$-statistical pre-Cauchy and $I^{*}$-statistical Cauchy sequences and explore its connection to $I^{*}$-statistical convergence. Our results show that every $I^{*}$-statistically convergent sequence is $I^{*}$-statistically pre-Cauchy, but the converse is not necessarily true. Furthermore, we provide a sufficient condition for an $I^{*}$-statistically pre-Cauchy sequence to be $I^{*}$-statistically convergent, which involves the concept of $I^{*}-liminf$.

We present the concepts of ideal monotonicity and ideal boundedness in the context of sequences involving fuzzy numbers. Additionally, we formulate an equivalent to the monotone convergence theorem and provide the necessary proofs for decomposition theorems specifically designed for this category of sequences. Our exploration extends to the analysis of I*-convergent sequences of fuzzy numbers, concluding with the demonstration that these sequences have a unique limit point.