Software systems accumulate technical debt over time as a result of short-term decisions that prioritize delivery speed over structural quality. For legacy systems this debt does not remain abstract. It becomes a concrete obstacle when organizations attempt to modernize, constraining what methods are viable and how much effort they require. This thesis investigates how different types of technical debt affect the modernization of legacy systems. While prior research has examined technical debt management in isolation, and modernization methods in isolation, the relationship between the two has received limited systematic attention. The central argument of this thesis is that the impact of technical debt on modernization is neither uniform nor inevitable, but rather it is contingent on the chosen method. The same debt type can be negligible for one approach and a hard blocker for another. To investigate this, the thesis combines a systematic literature review with practitioner interviews. The review covers 15 distinct technical debt types and maps their individual manifestations to the seven modernization methods. For each combination, an impact level is assigned — Limited, Significant, or Blocking — based on synthesized evidence from the literature, supplemented by author input where direct evidence is absent. These findings are organized into a structured assessment tool that makes the debt-method interaction explicit and navigable. The tool is consisting of two main modes, which allows practitioners to explore how resolving individual debt manifestations changes their risk level for a chosen method in real time. The primary contribution of this thesis is the demonstration that modernization method must be treated as a first-class variable in all technical debt assessments. A debt profile that leaves one method entirely viable may render another non-executable. This contingency has direct practical implications: organizations assessing their systems’ readiness for modernization need structured tools that make these dependencies visible before decisions are made, rather than discovering critical blockers mid-execution.