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Machine learning-driven decision support tools ought to assist users in detecting prospective advantages and risks. Widespread model-independent explanation frameworks can pinpoint elements underpinning specific forecasts—addressing queries like "What factors affect sales?" or "Why did sales decline?"—yet fail to indicate the actionable steps an individual can or should take to achieve a more favorable result. Although counterfactual explanation methods target actionable interventions and occasionally account for feasibility, they generally overlook viability for real-time systems like interactive question-answering platforms. In this study, we bridge this divide by proposing a unique model-agnostic technique that details concrete, viable adjustments capable of altering the predictions of a sophisticated Black Box AI framework for a specific instance; furthermore, we evaluate its practical utility by evaluating its computational speed in real time alongside its capacity to discover executable modifications. The developed approach leverages the target instance to produce high-precision explanations, subsequently employing a secondary mechanism to identify achievable minimally-contrastive counterfactual explanations (AMCC) by restricting the exploration space exclusively to modifications that adhere to domain-specific restrictions. Utilizing a well-established dataset, we assessed the classification procedure to determine both the success rate and the computational duration necessary to pinpoint valid counterfactual alternatives. Within the context of a classifier maintaining 90% accuracy, our proposed algorithm successfully extracted AMCC explanations in 47% of instances (38 of 81), requiring an average generation time of 80 ms. These outcomes confirm the efficiency of the algorithm in rapidly generating AMCC explanations, establishing its appropriateness for real-time deployments. The AMCC approach improves the interpretability of obscure Black Box AI models, thereby assisting stakeholders in analyzing corrective techniques or evaluating prospective consequences.