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This work comprehensively investigates the systematic integration of highly relevant life events and physiological parameters with continuous glucose monitoring (CGM) data to understand the synergy in glucoregulatory systems. Multitask learning (MTL) is employed in learning from numerous subjects while tailoring features in patient-specific layers. Systematic combinations of inputs are fed to the system providing personalized blood glucose level (BGL) predictions at multi-step prediction horizons (PHs) as output. Three cutting-edge long-short-term memory (LSTM) networks are adapted in the shared layers of MTL architecture. Moreover, PHs are varied with 30-minute intervals up to 120 minutes to identify the long-term effects of relevant input features on BGL prediction suggesting optimal deep learning (DL) architecture. The empirical result demonstrates that the most relevant features for BGL prediction are glucose, bolus insulin, and carbohydrate estimate from meals, while exercise and basal insulin rate have momentary effects. The best predictive root means square error (RMSE) achieved are $16.06~\pm ~2.74$ , $30.89~\pm ~4.31$ , $40.51~\pm ~5.16$ , and $47.31~\pm ~5.78$ mg/dL for 30-, 60-, 90-, and 120-min PH, respectively, while maintaining $94.06~\pm ~3.08$ % predictions in clinically safe zones (A + B) at 120-min PH in Clarke error grid analysis (EGA). The insights learned from the experiments will assist in selecting appropriate DL models, features, and timelines based on specific needs, with significant promise in improving T1D management through better therapeutic and lifestyle modification.