<p>As cutting-edge knowledge advances, a vast amount of subjective, non-absolute, and heterogeneous information is integrated into systems, and the decision-making process is becoming increasingly complex. Studies aiming to develop approaches to understand the relationships among these data are crucial to analyze how the system evolves over time and determine the best course of action in a given situation. Motivated by applications in modeling Inverse Problems related to computerized tomography using Fuzzy Cognitive Maps, the present work focuses on computational aspects of fuzzy optimization, and understanding how the uncertainties associated with these measurements influence image reconstruction. In this context, uncertainties were modeled by fuzzy numbers, which were adjusted through the <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\gamma \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>γ</mi> </math></EquationSource> </InlineEquation>-parameter associated with the representation of its <InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\alpha \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation>-cuts, and the analysis of these uncertainties was provided. For this purpose, the <InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(\gamma \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>γ</mi> </math></EquationSource> </InlineEquation>-parameter was optimized using synthetic tomographic data via Optuna. As a result, multiple promising analyses were conducted, expanding the potential for future studies with real computerized tomography data.</p>