Protracted armed conflicts in the Donbas region continue to severely impact the infrastructure of basic and essential services in various cities. Rebuilding these critical services during or after a conflict is a complex task, further complicated by protracted hostile situations with changing demands. Existing recovery and resilience frameworks are mostly based on natural hazards through design and intervention; however, they are poorly suited for use in post-conflict resilience. In addition, certain other recovery frameworks focus mainly on social impacts and their mitigation. Given this gap in knowledge and becoming the main motivation, the present study proposes a novel framework for recovery and resilience in post-conflict reconstruction. A new resilience term based on impeding factors (RBF) is introduced. Furthermore, the study mainly focuses on the proposal of using machine learning techniques such as computer vision and assessment algorithms for damage recognition, damage types, and classification for the reconstruction process during or after conflict. These artificial intelligence techniques are wrapped up within a second term introduced called resilience based on modified assessment (MRBA). The Fast-Track construction methodology is also incorporated into the framework path, justifying its use in the ability to superimpose design and construction phases, which significantly accelerates the reconstruction of critical infrastructure and provides essential services such as healthcare, shelter, and food supply. Likewise, in an armed conflict given a changing and unpredictable terrain environment, the flexibility and adaptability of the Fast-Track method allows for real-time adjustments to reconstruction. This study analyzes why conflicts and war require a new framework to achieve postconflict resilience. The proposal is applied to the Odesa-Ukraine area in a quantitative manner to demonstrate how the proposed framework using a modified resilience-by-assessment can help decision-makers address infrastructure needs and accelerate financial assistance. It also shows how the framework can absorb uncertainties and maximize recovery from post-conflict reconstruction.