  Context: Quantum software systems represent a new realm in software
engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to
solve the complex problems more efficiently than classical counterparts . Agile
software development approaches are considered to address many inherent
challenges in quantum software development, but their effective integration
remains unexplored Objective: This study investigates key causes of challenges
that could hinders the adoption of traditional agile approaches in quantum
software projects and develop an Agile Quantum Software Project Success
Prediction Model (AQSSPM). Methodology: Firstly, w e identified 19 causes of
challenging factors discussed in our previous study, which are potentially
impacting agile quantum project success. Secondly, a survey was conducted to
collect expert opinions on these causes and applied Genetic Algorithm (GA) with
Na i ve Bayes Classifier (NBC) and Logistic Regression (LR) to develop the
AQSSPM Results: Utilizing GA with NBC, project success probability improved
from 53.17% to 99.68%, with cost reductions from 0.463% to 0.403%. Similarly,
GA with LR increased success rates from 55.52% to 98.99%, and costs decreased
from 0.496% to 0.409% after 100 iterati ons. Both methods result showed a
strong positive correlation (rs=0.955) in causes ranking, with no significant
difference between them (t=1.195, p=0.240>0.05). Conclusion: The AQSSPM
highlights critical focus areas for efficiently and successfully implementing
agile quantum projects considering the cost factor of a particular project
