Prostate adenocarcinoma (PCa) is one among the most common global disorders affecting men and is a leading cause of cancer related mortality. In the present study, TP53, PTEN and AR genes in 680 histologically confirmed prostate cancer patients and 500 controls from Pakistan were investigated to unravel their role in prostate cancer. Exon specific primers were designed for all the exons of the three genes along with flanking intronic and UTR regions. Sequence changes were initially screened by PCR-SSCP and then confirmed through automated sequencing. Our data showed that none of the samples had a complete deletion of any of the three genes. A total of twenty six novel exonic mutations and eight novel intronic variants were detected in the target genes along with the four previously reported exonic alterations. The intronic variants were observed both in cases and controls and thus were further investigated for their possible association with the disease through un-conditional logistic regression under different genetic models. The g.7675016T>A variant of TP53 showed significant association with increased PCa risk under allelic contrast (OR=1.84, 95%CI=1.29-2.63, p-value=0.001), heterozygous model (OR=2.25, 95%CI=1.37-3.69, p-value=0.001) and dominant model (OR=2.13, 95%CI=1.38-3.28, p-value=0.001) while its g.7674991T>A variant showed positive association in all genetic models except TT vs TC and recessive model. In our studied population, we observed non-significant association for g.87891382G>A variant of PTEN in all models except allelic contrast (OR=1.87, 95%CI=1.36-2.58, p-value=0.000), while the other three variants of PTEN showed positive correlation with increased PCa risk. Similarly, both the intronic variants of AR gene were also found associated with increased PCa risk. The AA genotypes of g.67637091T>A variant and CC of g.67724021T>C variant are more common in cases. Homozygous individuals for A allele has 3.68 times higher risk of prostate cancer. Majority of the mutations are novel and thus establishing their pathogenicity is of prime diagnostic importance. A comprehensive structural and functional annotation was therefore reported in the present study for the observed mutations through the application of bioinformatics approaches. It was noticed that 5/8 of TP53 mutations (P152A, T170M, E171R, H179Q and V203G), 7/9 of PTEN mutations (A86P, E91K, H93Q, Q97H, E99X, H272F and E288F), while 11/13 of the AR gene mutations (I870fs, I870_splice, D880Y, E884X, I900V, S909C, K906N, L908P, V912G, I915F and Y916S) were predicted to endorse changes in their respective protein structure and thus are functionally damaging. Phenotypic data was collected in integration with the genotypic data from all the study participants and was statistically evaluated for their correlation and impact on individual’s survival. Age, smoking CVDs, BMI, dairy products consumption, physical activity, family history and hypertension was observed responsible for increasing prostate cancer risk under uni-variate and multi-variate models. The diabetic patients were observed at lower risk of having PCa while others risk factors were found non-associated. Overall survival analysis showed a median survival time of 21 months for the PCa patients (95%CI= 18-26). Intra-group differences in patient’s survival after adjusting for confounder age were confirmed through a proportional hazard model. It was observed that age, BMI, smoking, prostectomy and physical activities are associated with survival probability while the rest of risk factors have no effect on survival. It was further noticed that diabetic patients has better survival length as compared to the non-diabetic counterparts. Our results elucidate the significance of combining molecular and in silico approaches to fully distinguish pathogenic mutations from benign which will have profound effects on the patient management. We also have developed an integrated platform, pakprostate.com during the course of study to provide users the facility to search literature, genes, drugs, to have access to download verified dataset, can upload data, can apply basic stats and visualize data just on click of a button. The repository pakprostate is freely available online at the URL www.pakprostate.com. Case-control samples were collected from all the four provinces of Pakistan including Capital territory, Gilgit-baltistan and Kashmir. It is thus hoped that, this study is a good representation of Pakistani population.