Marine cyanobacteria have emerged as promising feedstocks for biofuels and valuable products; however, determining optimum biomass production under dynamic natural condition is crucial for commercialisation. Implementing predictive growth models enables to comprehend the interrelation between multidimensional variables and cyanobacterial biomass production. Thus, this investigation aimed to develop a predictive growth model for marine cyanobacterium Leptolyngbyavalderiana BDU 41001 cultured for two years under semi-outdoor settings with a translucent rooftop cover to limit high sunlight and prevent rainfall, where equivalent biomass yield with the laboratory condition was recorded during summer and winter seasons, indicating thermotolerant nature of the test cyanobacterium. The Principal Component Analysis of cultivation variables showed that solar intensity, temperature, relative humidity, dissolved oxygen, and nitrate concentration impacted 97% of data variations. Subsequently, Multiple Regression Analysis with these predictors showed 0.97 predictive R2 and <0.0001 P-value, confirming the significance of the model. Notably, achieving 92–94% prediction efficiencies for biomass production of three other marine cyanobacterial species and 90% for a consortium of microalgae cultivated at the sub-arctic climate of Umeå, Sweden, highlighted the potential of the developed model. Moreover, under semi-outdoor cultivation, a 10% rise in C-phycocyanin yield and an equivalent bioethanol production as compared to the laboratory cultures unravel the potential of L.valderiana BDU 41001 as a model cyanobacterium for multifaceted pilot-scale investigations under a bio-refinery approach.